Sensors

2025

Jump to: 2024, 2023, 2022

15 pages, 5273 KB

Open AccessArticle

Scale-Adaptive High-Resolution Imaging Using a Rotating-Prism-Guided Variable-Boresight Camera

by Zhaojun Deng, Anhu Li, Xin Zhao, Yonghao Lai and Jialiang Jin

Sensors 2025, 25(20), 6313; https://doi.org/10.3390/s25206313 - 12 Oct 2025

Viewed by 466

Abstract

Large-field-of-view (FOV) and high-resolution imaging have always been the goals pursued by imaging technology. A scale-adaptive high-resolution imaging architecture is established using a rotating-prism-embedded variable-boresight camera. By planning to prism motion, the multi-view images with rich information are combined to form a large-scale [...] Read more.

Large-field-of-view (FOV) and high-resolution imaging have always been the goals pursued by imaging technology. A scale-adaptive high-resolution imaging architecture is established using a rotating-prism-embedded variable-boresight camera. By planning to prism motion, the multi-view images with rich information are combined to form a large-scale FOV image. The boresight is guided towards the region of interest (ROI) in the combined FOV to reconstruct super-resolution (SR) images with the desired information. A novel distortion correction method is proposed using virtual symmetrical prisms with rotation angles that are complementary. Based on light reverse tracing, the dispersion induced by monochromatic lights with different refractive indices can be eliminated by accurate pixel-level position compensation. For resolution enhancement, we provide a new scheme for SR imaging consisting of the residual removal network and information enhancement network by multi-view image fusion. The experiments show that the proposed architecture can achieve both large-FOV scene imaging for situational awareness and SR ROI display to acquire details, effectively perform distortion and dispersion correction, and alleviate the occlusion to a certain extent. It also provides higher image clarity compared to the traditional SR methods and overcomes the problem of balancing large-scale imaging and high-resolution imaging. Full article

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17 pages, 52501 KB

Open AccessArticle

Single Shot High-Accuracy Diameter at Breast Height Measurement with Smartphone Embedded Sensors

by Wang Xiang, Songlin Fei and Song Zhang

Sensors 2025, 25(16), 5060; https://doi.org/10.3390/s25165060 - 14 Aug 2025

Viewed by 570

Abstract

Tree diameter at breast height (DBH) is a fundamental metric in forest inventory and management. This paper presents a novel method for DBH estimation using the built-in light detection and ranging (LiDAR) and red, green and blue (RGB) sensors of an iPhone 13 [...] Read more.

Tree diameter at breast height (DBH) is a fundamental metric in forest inventory and management. This paper presents a novel method for DBH estimation using the built-in light detection and ranging (LiDAR) and red, green and blue (RGB) sensors of an iPhone 13 Pro, aiming to improve measurement accuracy and field usability. A single snapshot of a tree, capturing both depth and RGB images, is used to reconstruct a 3D point cloud. The trunk orientation is estimated based on the point cloud to locate the breast height, enabling robust DBH estimation independent of the capture angle. The DBH is initially estimated by the geometrical relationship between trunk size on the image and the depth of the trunk. Finally, a pre-computed lookup table (LUT) is employed to improve the initial DBH estimates into accurate values. Experimental evaluation on 294 trees within a capture range of 0.25 m to 5 m demonstrates a mean absolute error of 0.53 cm and a root mean square error of 0.63 cm. Full article

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9 pages, 3150 KB

Open AccessArticle

Combined Structural and Functional 3D Plant Imaging Using Structure from Motion

by Alim Yolalmaz, Jos de Wit and Jeroen Kalkman

Sensors 2025, 25(5), 1572; https://doi.org/10.3390/s25051572 - 4 Mar 2025

Viewed by 1054

Abstract

We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the [...] Read more.

We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the images to increase the number of key points. With the same setup, we obtain functional fluorescence information that we map onto the 3D structural plant image, in this way obtaining a combined functional and 3D structural plant image using a single setup. Full article

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15 pages, 8197 KB

Open AccessArticle

Does Scanner Choice Matter for the Design of Foot Orthosis?

by Komal Chhikara, Sinduja Suresh, Scott Morrison, Dean Hartley, Kerrie Evans, Marie-Luise Wille, Müge Belek Fialho Teixeira, Bridget Hughes, Natalie Haskell, Amanda Beatson, Marianella Chamorro-Koc and Judith Paige Little

Sensors 2025, 25(3), 869; https://doi.org/10.3390/s25030869 - 31 Jan 2025

Viewed by 2032

Abstract

A variety of 3D volumetric scanners and smart-device applications are currently being used in podiatry for recording virtual foot data. The accuracy and reliability of these devices vary, resulting in a large variation in the quality of foot scans used for orthotic design. [...] Read more.

A variety of 3D volumetric scanners and smart-device applications are currently being used in podiatry for recording virtual foot data. The accuracy and reliability of these devices vary, resulting in a large variation in the quality of foot scans used for orthotic design. While it is widely believed that a higher quality scanner yields a better scan and thus is expected to produce a more accurate orthotic design, the direct impact of scanning quality on orthotic design has not yet been tested. Therefore, in this study, three commonly used industrial 3D scanners with varying output qualities were used to obtain foot scans of three participants in two weight-bearing conditions. A total of 54 foot scans were obtained, out of which 18 were used to design orthotic insoles using commercial software (FitFoot360). We found variation in the quality of foot scans produced by the different scanners (61.75 ± 2.23% similarity of the foot scans showing a deviation of less than ±1 mm). However, there were no significant differences in the designed foot orthoses within the same weight-bearing condition (83.59 ± 1.97% similarity of the orthotic designs showing a deviation of less than ±1 mm). The medial arch height and heel width differed significantly only when the weight-bearing condition was changed. The findings from this study suggest that the industrial design and production of an orthotic insole using current methods does not depend on the scanning quality of the scanner used but is dependent on the extent of weight bearing. Full article

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19 pages, 5153 KB

Open AccessArticle

Aluminum Reservoir Welding Surface Defect Detection Method Based on Three-Dimensional Vision

by Hanjie Huang, Bin Zhou, Songxiao Cao, Tao Song, Zhipeng Xu and Qing Jiang

Sensors 2025, 25(3), 664; https://doi.org/10.3390/s25030664 - 23 Jan 2025

Cited by 2 | Viewed by 1563

Abstract

Welding is an important process in the production of aluminum reservoirs for motor vehicles. The welding quality affects product performance. However, rapid and accurate detection of weld surface defects remains a huge challenge in the field of industrial automation. To address this problem, [...] Read more.

Welding is an important process in the production of aluminum reservoirs for motor vehicles. The welding quality affects product performance. However, rapid and accurate detection of weld surface defects remains a huge challenge in the field of industrial automation. To address this problem, we proposed a 3D vision-based aluminum reservoir welding surface defect detection method. First of all, a scanning system based on laser line scanning camera was constructed to acquire the point cloud data of weld seams on the aluminum reservoir surface. Next, a planar correction algorithm was used to adjust the slope of the contour line according to the slope of the contour line in order to minimize the effect of systematic disturbances when acquiring weld data. Then, the surface features of the weld, including curvature and normal vector direction, were extracted to extract holes, craters, and undercut defects. For better extraction of the defect, a double-aligned template matching method was used to ensure comprehensive extraction and measurement of defect areas. Finally, the detected defects were categorized according to their morphology. Experimental results show that the proposed method using 3D laser scanning data can detect and classify typical welding defects with an accuracy of more than 97.1%. Furthermore, different types of defects, including holes, undercuts, and craters, can also be accurately detected with precision 98.9%. Full article

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2024

Jump to: 2025, 2023, 2022

20 pages, 7065 KB

Open AccessArticle

Laser Scan Compression for Rail Inspection

by Jeremiasz Hauck and Piotr Gniado

Sensors 2024, 24(20), 6722; https://doi.org/10.3390/s24206722 - 19 Oct 2024

Cited by 2 | Viewed by 2336

Abstract

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for [...] Read more.

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for laser triangulation scanners, which are crucial for scanning the entire rail track, including the rails, rail fasteners, sleepers, and ballast, and capturing rail profiles for geometry measurement. The compression technique capitalizes on the regularity of rail track data and the sensors’ limited measurement range and resolution. By transforming scans, they can be stored using widely available image compression formats, such as PNG. This method achieved a compression ratio of 7.5 for rail scans used in the rail geometry computation and maintained rail gauge reproducibility. For the scans employed in defect detection, a compression ratio of 5.6 was attained without visibly compromising the scan quality. Lossless compression resulted in compression ratios of 5.1 for the rail geometry computation scans and 3.8 for the rail track inspection scans. Full article

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19 pages, 7867 KB

Open AccessArticle

Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural Networks

by Mohamed Fathallah, Sherif Eletriby, Maazen Alsabaan, Mohamed I. Ibrahem and Gamal Farok

Sensors 2024, 24(19), 6280; https://doi.org/10.3390/s24196280 - 28 Sep 2024

Cited by 1 | Viewed by 7707

Abstract

This paper presents a novel framework for 3D face reconstruction from single 2D images and addresses critical limitations in existing methods. Our approach integrates modified adversarial neural networks with graph neural networks to achieve state-of-the-art performance. Key innovations include (1) a generator architecture [...] Read more.

This paper presents a novel framework for 3D face reconstruction from single 2D images and addresses critical limitations in existing methods. Our approach integrates modified adversarial neural networks with graph neural networks to achieve state-of-the-art performance. Key innovations include (1) a generator architecture based on Graph Convolutional Networks (GCNs) with a novel loss function and identity blocks, mitigating mode collapse and instability; (2) the integration of facial landmarks and a non-parametric efficient-net decoder for enhanced feature capture; and (3) a lightweight GCN-based discriminator for improved accuracy and stability. Evaluated on the 300W-LP and AFLW2000-3D datasets, our method outperforms existing approaches, reducing Chamfer Distance by 62.7% and Earth Mover’s Distance by 57.1% on 300W-LP. Moreover, our framework demonstrates superior robustness to variations in head positioning, occlusion, noise, and lighting conditions while achieving significantly faster processing times. Full article

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21 pages, 16376 KB

Open AccessArticle

Triple-Camera Rectification for Depth Estimation Sensor

by Minkyung Jeon, Jinhong Park, Jin-Woo Kim and Sungmin Woo

Sensors 2024, 24(18), 6100; https://doi.org/10.3390/s24186100 - 20 Sep 2024

Cited by 2 | Viewed by 1785

Abstract

In this study, we propose a novel rectification method for three cameras using a single image for depth estimation. Stereo rectification serves as a fundamental preprocessing step for disparity estimation in stereoscopic cameras. However, off-the-shelf depth cameras often include an additional RGB camera [...] Read more.

In this study, we propose a novel rectification method for three cameras using a single image for depth estimation. Stereo rectification serves as a fundamental preprocessing step for disparity estimation in stereoscopic cameras. However, off-the-shelf depth cameras often include an additional RGB camera for creating 3D point clouds. Existing rectification methods only align two cameras, necessitating an additional rectification and remapping process to align the third camera. Moreover, these methods require multiple reference checkerboard images for calibration and aim to minimize alignment errors, but often result in rotated images when there is significant misalignment between two cameras. In contrast, the proposed method simultaneously rectifies three cameras in a single shot without unnecessary rotation. To achieve this, we designed a lab environment with checkerboard settings and obtained multiple sample images from the cameras. The optimization function, designed specifically for rectification in stereo matching, enables the simultaneous alignment of all three cameras while ensuring performance comparable to traditional methods. Experimental results with real camera samples demonstrate the benefits of the proposed method and provide a detailed analysis of unnecessary rotations in the rectified images. Full article

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17 pages, 13477 KB

Open AccessArticle

Hybrid Bright-Dark-Field Microscopic Fringe Projection System for Cu Pillar Height Measurement in Wafer-Level Package

by Dezhao Wang, Weihu Zhou, Zili Zhang and Fanchang Meng

Sensors 2024, 24(16), 5157; https://doi.org/10.3390/s24165157 - 9 Aug 2024

Viewed by 1897

Abstract

Cu pillars serve as interconnecting structures for 3D chip stacking in heterogeneous integration, whose height uniformity directly impacts chip yield. Compared to typical methods such as white-light interferometry and confocal microscopy for measuring Cu pillars, microscopic fringe projection profilometry (MFPP) offers obvious advantages [...] Read more.

Cu pillars serve as interconnecting structures for 3D chip stacking in heterogeneous integration, whose height uniformity directly impacts chip yield. Compared to typical methods such as white-light interferometry and confocal microscopy for measuring Cu pillars, microscopic fringe projection profilometry (MFPP) offers obvious advantages in throughput, which has great application value in on-line bump height measurement in wafer-level packages. However, Cu pillars with large curvature and smooth surfaces pose challenges for signal detection. To enable the MFPP system to measure both the top region of the Cu pillar and the substrate, which are necessary for bump height measurement, we utilized rigorous surface scattering theory to solve the bidirectional reflective distribution function of the Cu pillar surface. Subsequently, leveraging the scattering distribution properties, we propose a hybrid bright-dark-field MFPP system concept capable of detecting weakly scattered signals from the top of the Cu pillar and reflected signals from the substrate. Experimental results demonstrate that the proposed MFPP system can measure the height of Cu pillars with an effective field of view of 15.2 mm × 8.9 mm and a maximum measurement error of less than 0.65 μm. Full article

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20 pages, 10566 KB

Open AccessArticle

DELTA: Integrating Multimodal Sensing with Micromobility for Enhanced Sidewalk and Pedestrian Route Understanding

by Alireza Akhavi Zadegan, Damien Vivet and Amnir Hadachi

Sensors 2024, 24(12), 3863; https://doi.org/10.3390/s24123863 - 14 Jun 2024

Cited by 4 | Viewed by 1721

Abstract

Urban environments are undergoing significant transformations, with pedestrian areas emerging as complex hubs of diverse mobility modes. This shift demands a more nuanced approach to urban planning and navigation technologies, highlighting the limitations of traditional, road-centric datasets in capturing the detailed dynamics of [...] Read more.

Urban environments are undergoing significant transformations, with pedestrian areas emerging as complex hubs of diverse mobility modes. This shift demands a more nuanced approach to urban planning and navigation technologies, highlighting the limitations of traditional, road-centric datasets in capturing the detailed dynamics of pedestrian spaces. In response, we introduce the DELTA dataset, designed to improve the analysis and mapping of pedestrian zones, thereby filling the critical need for sidewalk-centric multimodal datasets. The DELTA dataset was collected in a single urban setting using a custom-designed modular multi-sensing e-scooter platform encompassing high-resolution and synchronized audio, visual, LiDAR, and GNSS/IMU data. This assembly provides a detailed, contextually varied view of urban pedestrian environments. We developed three distinct pedestrian route segmentation models for various sensors—the 4K camera, stereocamera, and LiDAR—each optimized to capitalize on the unique strengths and characteristics of the respective sensor. These models have demonstrated strong performance, with Mean Intersection over Union (IoU) values of 0.84 for the reflectivity channel, 0.96 for the 4K camera, and 0.92 for the stereocamera, underscoring their effectiveness in ensuring precise pedestrian route identification across different resolutions and sensor types. Further, we explored audio event-based classification to connect unique soundscapes with specific geolocations, enriching the spatial understanding of urban environments by associating distinctive auditory signatures with their precise geographical origins. We also discuss potential use cases for the DELTA dataset and the limitations and future possibilities of our research, aiming to expand our understanding of pedestrian environments. Full article

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20 pages, 2695 KB

Open AccessArticle

Image Reconstruction Requirements for Short-Range Inductive Sensors Used in Single-Coil MIT

by Joe R. Feldkamp

Sensors 2024, 24(9), 2704; https://doi.org/10.3390/s24092704 - 24 Apr 2024

Viewed by 1300

Abstract

MIT (magnetic induction tomography) image reconstruction from data acquired with a single, small inductive sensor has unique requirements not found in other imaging modalities. During the course of scanning over a target, measured inductive loss decreases rapidly with distance from the target boundary. [...] Read more.

MIT (magnetic induction tomography) image reconstruction from data acquired with a single, small inductive sensor has unique requirements not found in other imaging modalities. During the course of scanning over a target, measured inductive loss decreases rapidly with distance from the target boundary. Since inductive loss exists even at infinite separation due to losses internal to the sensor, all other measurements made in the vicinity of the target require subtraction of the infinite-separation loss. This is accomplished naturally by treating infinite-separation loss as an unknown. Furthermore, since contributions to inductive loss decline with greater depth into a conductive target, regularization penalties must be decreased with depth. A pair of squared L2 penalty norms are combined to form a 2-term Sobolev norm, including a zero-order penalty that penalizes solution departures from a default solution and a first-order penalty that promotes smoothness. While constraining the solution to be non-negative and bounded from above, the algorithm is used to perform image reconstruction on scan data obtained over a 4.3 cm thick phantom consisting of bone-like features embedded in agarose gel, with the latter having a nominal conductivity of 1.4 S/m. Full article

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15 pages, 6531 KB

Open AccessArticle

Range-Intensity-Profile-Guided Gated Light Ranging and Imaging Based on a Convolutional Neural Network

by Chenhao Xia, Xinwei Wang, Liang Sun, Yue Zhang, Bo Song and Yan Zhou

Sensors 2024, 24(7), 2151; https://doi.org/10.3390/s24072151 - 27 Mar 2024

Cited by 4 | Viewed by 1688

Abstract

Three-dimensional (3D) range-gated imaging can obtain high spatial resolution intensity images as well as pixel-wise depth information. Several algorithms have been developed to recover depth from gated images such as the range-intensity correlation algorithm and deep-learning-based algorithm. The traditional range-intensity correlation algorithm requires [...] Read more.

Three-dimensional (3D) range-gated imaging can obtain high spatial resolution intensity images as well as pixel-wise depth information. Several algorithms have been developed to recover depth from gated images such as the range-intensity correlation algorithm and deep-learning-based algorithm. The traditional range-intensity correlation algorithm requires specific range-intensity profiles, which are hard to generate, while the existing deep-learning-based algorithm requires large number of real-scene training data. In this work, we propose a method of range-intensity-profile-guided gated light ranging and imaging to recover depth from gated images based on a convolutional neural network. In this method, the range-intensity profile (RIP) of a given gated light ranging and imaging system is obtained to generate synthetic training data from Grand Theft Auto V for our range-intensity ratio and semantic network (RIRS-net). The RIRS-net is mainly trained on synthetic data and fine-tuned with RIP data. The network learns both semantic depth cues and range-intensity depth cues in the synthetic data, and learns accurate range-intensity depth cues in the RIP data. In the evaluation experiments on both a real-scene and synthetic test dataset, our method shows a better result compared to other algorithms. Full article

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14 pages, 9349 KB

Open AccessArticle

3D Digital Modeling of Dental Casts from Their 3D CT Images with Scatter and Beam-Hardening Correction

by Mohamed A. A. Hegazy, Myung Hye Cho, Min Hyoung Cho and Soo Yeol Lee

Sensors 2024, 24(6), 1995; https://doi.org/10.3390/s24061995 - 21 Mar 2024

Cited by 1 | Viewed by 2106

Abstract

Dental 3D modeling plays a pivotal role in digital dentistry, offering precise tools for treatment planning, implant placement, and prosthesis customization. Traditional methods rely on physical plaster casts, which pose challenges in storage, accessibility, and accuracy, fueling interest in digitization using 3D computed [...] Read more.

Dental 3D modeling plays a pivotal role in digital dentistry, offering precise tools for treatment planning, implant placement, and prosthesis customization. Traditional methods rely on physical plaster casts, which pose challenges in storage, accessibility, and accuracy, fueling interest in digitization using 3D computed tomography (CT) imaging. We introduce a method that can reduce both artifacts simultaneously. To validate the proposed method, we carried out CT scan experiments using plaster dental casts created from dental impressions. After the artifact correction, the CT image quality was greatly improved in terms of image uniformity, contrast-to-noise ratio (CNR), and edge sharpness. We examined the correction effects on the accuracy of the 3D models generated from the CT images. As referenced to the 3D models derived from the optical scan data, the root mean square (RMS) errors were reduced by 8.8~71.7% for three dental casts of different sizes and shapes. Our method offers a solution to challenges posed by artifacts in CT scanning of plaster dental casts, leading to enhanced 3D model accuracy. This advancement holds promise for dental professionals seeking precise digital modeling for diverse applications in dentistry. Full article

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15 pages, 21175 KB

Open AccessArticle

Pre-Segmented Down-Sampling Accelerates Graph Neural Network-Based 3D Object Detection in Autonomous Driving

by Zhenming Liang, Yingping Huang and Yanbiao Bai

Sensors 2024, 24(5), 1458; https://doi.org/10.3390/s24051458 - 23 Feb 2024

Cited by 4 | Viewed by 2119

Abstract

Graph neural networks (GNNs) have been proven to be an ideal approach to deal with irregular point clouds, but involve massive computations for searching neighboring points in the graph, which limits their application in large-scale LiDAR point cloud processing. Down-sampling is a straightforward [...] Read more.

Graph neural networks (GNNs) have been proven to be an ideal approach to deal with irregular point clouds, but involve massive computations for searching neighboring points in the graph, which limits their application in large-scale LiDAR point cloud processing. Down-sampling is a straightforward and indispensable step in current GNN-based 3D detectors to reduce the computational burden of the model, but the commonly used down-sampling methods cannot distinguish the categories of the LiDAR points, which leads to an inability to effectively improve the computational efficiency of the GNN models without affecting their detection accuracy. In this paper, we propose (1) a LiDAR point cloud pre-segmented down-sampling (PSD) method that can selectively reduce background points while preserving the foreground object points during the process, greatly improving the computational efficiency of the model without affecting its 3D detection accuracy. (2) A lightweight GNN-based 3D detector that can extract point features and detect objects from the raw down-sampled LiDAR point cloud directly without any pre-transformation. We test the proposed model on the KITTI 3D Object Detection Benchmark, and the results demonstrate its effectiveness and efficiency for autonomous driving 3D object detection. Full article

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2023

Jump to: 2025, 2024, 2022

19 pages, 5498 KB

Open AccessArticle

Integral Imaging Display System Based on Human Visual Distance Perception Model

by Lijin Deng, Zhihong Li, Yuejianan Gu and Qi Wang

Sensors 2023, 23(21), 9011; https://doi.org/10.3390/s23219011 - 6 Nov 2023

Cited by 4 | Viewed by 2428

Abstract

In an integral imaging (II) display system, the self-adjustment ability of the human eye can result in blurry observations when viewing 3D targets outside the focal plane within a specific range. This can impact the overall imaging quality of the II system. This [...] Read more.

In an integral imaging (II) display system, the self-adjustment ability of the human eye can result in blurry observations when viewing 3D targets outside the focal plane within a specific range. This can impact the overall imaging quality of the II system. This research examines the visual characteristics of the human eye and analyzes the path of light from a point source to the eye in the process of capturing and reconstructing the light field. Then, an overall depth of field (DOF) model of II is derived based on the human visual system (HVS). On this basis, an II system based on the human visual distance (HVD) perception model is proposed, and an interactive II display system is constructed. The experimental results confirm the effectiveness of the proposed method. The display system improves the viewing distance range, enhances spatial resolution and provides better stereoscopic display effects. When comparing our method with three other methods, it is clear that our approach produces better results in optical experiments and objective evaluations: the cumulative probability of blur detection (CPBD) value is 38.73%, the structural similarity index (SSIM) value is 86.56%, and the peak signal-to-noise ratio (PSNR) value is 31.12. These values align with subjective evaluations based on the characteristics of the human visual system. Full article

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16 pages, 9044 KB

Open AccessArticle

Edge Bleeding Artifact Reduction for Shape from Focus in Microscopic 3D Sensing

by Sang-Ho Park, Ga-Rin Park and Kwang-Ryul Baek

Sensors 2023, 23(20), 8602; https://doi.org/10.3390/s23208602 - 20 Oct 2023

Cited by 1 | Viewed by 1694

Abstract

Shape from focus enables microscopic 3D sensing by combining it with a microscope system. However, edge bleeding artifacts of estimated depth easily occur in this environment. Therefore, this study analyzed artifacts and proposed a method to reduce edge bleeding artifacts. As a result [...] Read more.

Shape from focus enables microscopic 3D sensing by combining it with a microscope system. However, edge bleeding artifacts of estimated depth easily occur in this environment. Therefore, this study analyzed artifacts and proposed a method to reduce edge bleeding artifacts. As a result of the analysis, the artifact factors are the depth of field of the lens, object texture, brightness difference between layers, and the slope of the object. Additionally, to reduce artifacts, a weighted focus measure value method was proposed based on the asymmetry of local brightness in artifacts. The proposed reduction method was evaluated through simulation and implementation. Edge bleeding artifact reduction rates of up to 60% were shown in various focus measure operators. The proposed method can be used with postprocessing algorithms and reduces edge bleeding artifacts. Full article

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15 pages, 1796 KB

Open AccessArticle

A 256 × 256 LiDAR Imaging System Based on a 200 mW SPAD-Based SoC with Microlens Array and Lightweight RGB-Guided Depth Completion Neural Network

by Jier Wang, Jie Li, Yifan Wu, Hengwei Yu, Lebei Cui, Miao Sun and Patrick Yin Chiang

Sensors 2023, 23(15), 6927; https://doi.org/10.3390/s23156927 - 3 Aug 2023

Cited by 4 | Viewed by 5732

Abstract

Light detection and ranging (LiDAR) technology, a cutting-edge advancement in mobile applications, presents a myriad of compelling use cases, including enhancing low-light photography, capturing and sharing 3D images of fascinating objects, and elevating the overall augmented reality (AR) experience. However, its widespread adoption [...] Read more.

Light detection and ranging (LiDAR) technology, a cutting-edge advancement in mobile applications, presents a myriad of compelling use cases, including enhancing low-light photography, capturing and sharing 3D images of fascinating objects, and elevating the overall augmented reality (AR) experience. However, its widespread adoption has been hindered by the prohibitive costs and substantial power consumption associated with its implementation in mobile devices. To surmount these obstacles, this paper proposes a low-power, low-cost, single-photon avalanche detector (SPAD)-based system-on-chip (SoC) which packages the microlens arrays (MLAs) and a lightweight RGB-guided sparse depth imaging completion neural network for 3D LiDAR imaging. The proposed SoC integrates an 8 × 8 SPAD macropixel array with time-to-digital converters (TDCs) and a charge pump, fabricated using a 180 nm bipolar-CMOS-DMOS (BCD) process. Initially, the primary function of this SoC was limited to serving as a ranging sensor. A random MLA-based homogenizing diffuser efficiently transforms Gaussian beams into flat-topped beams with a 45° field of view (FOV), enabling flash projection at the transmitter. To further enhance resolution and broaden application possibilities, a lightweight neural network employing RGB-guided sparse depth complementation is proposed, enabling a substantial expansion of image resolution from 8 × 8 to quarter video graphics array level (QVGA; 256 × 256). Experimental results demonstrate the effectiveness and stability of the hardware encompassing the SoC and optical system, as well as the lightweight features and accuracy of the algorithmic neural network. The state-of-the-art SoC-neural network solution offers a promising and inspiring foundation for developing consumer-level 3D imaging applications on mobile devices. Full article

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21 pages, 5685 KB

Open AccessArticle

A New Vision Measurement Technique with Large Field of View and High Resolution

by Yong Li, Chenguang Liu, Xiaoyu You and Jian Liu

Sensors 2023, 23(14), 6615; https://doi.org/10.3390/s23146615 - 23 Jul 2023

Cited by 4 | Viewed by 3054

Abstract

The three-dimensional (3D) displacement resolution of conventional visual measurement systems can only reach tens of microns in cases involving long measuring distances (2.5 m) and large fields of view (1.5 m × 1.5 m). Therefore, a stereo vision measurement technology based on confocal [...] Read more.

The three-dimensional (3D) displacement resolution of conventional visual measurement systems can only reach tens of microns in cases involving long measuring distances (2.5 m) and large fields of view (1.5 m × 1.5 m). Therefore, a stereo vision measurement technology based on confocal scanning is proposed herein. This technology combines macroscopic visual measurement technology with confocal microscopic measurement technology to achieve a long measuring distance, a large field of view, and micron-level measuring resolution. First, we analyzed the factors affecting the 3D resolution of the visual system and developed a 3D resolution model of the visual system. Subsequently, we fabricated a prototype based on the resolution model and the proposed stereo vision measurement technology. The 3D displacement resolution measurement results in the full field of view show that the displacement resolutions of the developed equipment in the x-, y-, and z-directions can reach 2.5, 2.5, and 6 μm, respectively. Full article

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15 pages, 598 KB

Open AccessArticle

A Fast and Reliable Solution to PnP, Using Polynomial Homogeneity and a Theorem of Hilbert

by Daniel Keren, Margarita Osadchy and Amit Shahar

Sensors 2023, 23(12), 5585; https://doi.org/10.3390/s23125585 - 14 Jun 2023

Cited by 2 | Viewed by 2139

Abstract

One of the most-extensively studied problems in three-dimensional Computer Vision is “Perspective-n-Point” (PnP), which concerns estimating the pose of a calibrated camera, given a set of 3D points in the world and their corresponding 2D projections in an image captured by the camera. [...] Read more.

One of the most-extensively studied problems in three-dimensional Computer Vision is “Perspective-n-Point” (PnP), which concerns estimating the pose of a calibrated camera, given a set of 3D points in the world and their corresponding 2D projections in an image captured by the camera. One solution method that ranks as very accurate and robust proceeds by reducing PnP to the minimization of a fourth-degree polynomial over the three-dimensional sphere

S^{3}

. Despite a great deal of effort, there is no known fast method to obtain this goal. A very common approach is solving a convex relaxation of the problem, using “Sum Of Squares” (SOS) techniques. We offer two contributions in this paper: a faster (by a factor of roughly 10) solution with respect to the state-of-the-art, which relies on the polynomial’s homogeneity; and a fast, guaranteed, easily parallelizable approximation, which makes use of a famous result of Hilbert. Full article

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17 pages, 12164 KB

Open AccessArticle

Robust Detection, Segmentation, and Metrology of High Bandwidth Memory 3D Scans Using an Improved Semi-Supervised Deep Learning Approach

by Jie Wang, Richard Chang, Ziyuan Zhao and Ramanpreet Singh Pahwa

Sensors 2023, 23(12), 5470; https://doi.org/10.3390/s23125470 - 9 Jun 2023

Cited by 11 | Viewed by 3495

Abstract

Recent advancements in 3D deep learning have led to significant progress in improving accuracy and reducing processing time, with applications spanning various domains such as medical imaging, robotics, and autonomous vehicle navigation for identifying and segmenting different structures. In this study, we employ [...] Read more.

Recent advancements in 3D deep learning have led to significant progress in improving accuracy and reducing processing time, with applications spanning various domains such as medical imaging, robotics, and autonomous vehicle navigation for identifying and segmenting different structures. In this study, we employ the latest developments in 3D semi-supervised learning to create cutting-edge models for the 3D object detection and segmentation of buried structures in high-resolution X-ray semiconductors scans. We illustrate our approach to locating the region of interest of the structures, their individual components, and their void defects. We showcase how semi-supervised learning is utilized to capitalize on the vast amounts of available unlabeled data to enhance both detection and segmentation performance. Additionally, we explore the benefit of contrastive learning in the data pre-selection step for our detection model and multi-scale Mean Teacher training paradigm in 3D semantic segmentation to achieve better performance compared with the state of the art. Our extensive experiments have shown that our method achieves competitive performance and is able to outperform by up to 16% on object detection and 7.8% on semantic segmentation. Additionally, our automated metrology package shows a mean error of less than 2

μ

m for key features such as Bond Line Thickness and pad misalignment. Full article

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16 pages, 63200 KB

Open AccessArticle

High-Capacity Spatial Structured Light for Robust and Accurate Reconstruction

by Feifei Gu, Hubing Du, Sicheng Wang, Bohuai Su and Zhan Song

Sensors 2023, 23(10), 4685; https://doi.org/10.3390/s23104685 - 12 May 2023

Cited by 8 | Viewed by 3648

Abstract

Spatial structured light (SL) can achieve three-dimensional measurements with a single shot. As an important branch in the field of dynamic reconstruction, its accuracy, robustness, and density are of vital importance. Currently, there is a wide performance gap of spatial SL between dense [...] Read more.

Spatial structured light (SL) can achieve three-dimensional measurements with a single shot. As an important branch in the field of dynamic reconstruction, its accuracy, robustness, and density are of vital importance. Currently, there is a wide performance gap of spatial SL between dense reconstruction (but less accurate, e.g., speckle-based SL) and accurate reconstruction (but often sparser, e.g., shape-coded SL). The central problem lies in the coding strategy and the designed coding features. This paper aims to improve the density and quantity of reconstructed point clouds by spatial SL whilst also maintaining a high accuracy. Firstly, a new pseudo-2D pattern generation strategy was developed, which can improve the coding capacity of shape-coded SL greatly. Then, to extract the dense feature points robustly and accurately, an end-to-end corner detection method based on deep learning was developed. Finally, the pseudo-2D pattern was decoded with the aid of the epipolar constraint. Experimental results validated the effectiveness of the proposed system. Full article

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19 pages, 10797 KB

Open AccessArticle

3D Object Detection via 2D Segmentation-Based Computational Integral Imaging Applied to a Real Video

by Michael Kadosh and Yitzhak Yitzhaky

Sensors 2023, 23(9), 4191; https://doi.org/10.3390/s23094191 - 22 Apr 2023

Cited by 4 | Viewed by 2869

Abstract

This study aims to achieve accurate three-dimensional (3D) localization of multiple objects in a complicated scene using passive imaging. It is challenging, as it requires accurate localization of the objects in all three dimensions given recorded 2D images. An integral imaging system captures [...] Read more.

This study aims to achieve accurate three-dimensional (3D) localization of multiple objects in a complicated scene using passive imaging. It is challenging, as it requires accurate localization of the objects in all three dimensions given recorded 2D images. An integral imaging system captures the scene from multiple angles and is able to computationally produce blur-based depth information about the objects in the scene. We propose a method to detect and segment objects in a 3D space using integral-imaging data obtained by a video camera array. Using objects’ two-dimensional regions detected via deep learning, we employ local computational integral imaging in detected objects’ depth tubes to estimate the depth positions of the objects along the viewing axis. This method analyzes object-based blurring characteristics in the 3D environment efficiently. Our camera array produces an array of multiple-view videos of the scene, called elemental videos. Thus, the proposed 3D object detection applied to the video frames allows for 3D tracking of the objects with knowledge of their depth positions along the video. Results show successful 3D object detection with depth localization in a real-life scene based on passive integral imaging. Such outcomes have not been obtained in previous studies using integral imaging; mainly, the proposed method outperforms them in its ability to detect the depth locations of objects that are in close proximity to each other, regardless of the object size. This study may contribute when robust 3D object localization is desired with passive imaging, but it requires a camera or lens array imaging apparatus. Full article

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22 pages, 13278 KB

Open AccessArticle

A Cylindrical Near-Field Acoustical Holography Method Based on Cylindrical Translation Window Expansion and an Autoencoder Stacked with 3D-CNN Layers

by Jiaxuan Wang, Weihan Zhang, Zhifu Zhang and Yizhe Huang

Sensors 2023, 23(8), 4146; https://doi.org/10.3390/s23084146 - 20 Apr 2023

Cited by 8 | Viewed by 2904

Abstract

The performance of near-field acoustic holography (NAH) with a sparse sampling rate will be affected by spatial aliasing or inverse ill-posed equations. Through a 3D convolution neural network (CNN) and stacked autoencoder framework (CSA), the data-driven CSA-NAH method can solve this problem by [...] Read more.

The performance of near-field acoustic holography (NAH) with a sparse sampling rate will be affected by spatial aliasing or inverse ill-posed equations. Through a 3D convolution neural network (CNN) and stacked autoencoder framework (CSA), the data-driven CSA-NAH method can solve this problem by utilizing the information from data in each dimension. In this paper, the cylindrical translation window (CTW) is introduced to truncate and roll out the cylindrical image to compensate for the loss of circumferential features at the truncation edge. Combined with the CSA-NAH method, a cylindrical NAH method based on stacked 3D-CNN layers (CS3C) for sparse sampling is proposed, and its feasibility is verified numerically. In addition, the planar NAH method based on the Paulis–Gerchberg extrapolation interpolation algorithm (PGa) is introduced into the cylindrical coordinate system, and compared with the proposed method. The results show that, under the same conditions, the reconstruction error rate of the CS3C-NAH method is reduced by nearly 50%, and the effect is significant. Full article

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21 pages, 4194 KB

Open AccessArticle

Digital Fringe Projection-Based Clamping Force Estimation Algorithm for Railway Fasteners

by Zhengji Fan, Yingping Hong, Yunfeng Wang, Yanan Niu, Huixin Zhang and Chengqun Chu

Sensors 2023, 23(6), 3299; https://doi.org/10.3390/s23063299 - 21 Mar 2023

Cited by 1 | Viewed by 2436

Abstract

The inspection of railway fasteners to assess their clamping force can be used to evaluate the looseness of the fasteners and improve railway safety. Although there are various methods for inspecting railway fasteners, there is still a need for non-contact, fast inspection without [...] Read more.

The inspection of railway fasteners to assess their clamping force can be used to evaluate the looseness of the fasteners and improve railway safety. Although there are various methods for inspecting railway fasteners, there is still a need for non-contact, fast inspection without installing additional devices on fasteners. In this study, a system that uses digital fringe projection technology to measure the 3D topography of the fastener was developed. This system inspects the looseness through a series of algorithms, including point cloud denoising, coarse registration based on fast point feature histograms (FPFH) features, fine registration based on the iterative closest point (ICP) algorithm, specific region selection, kernel density estimation, and ridge regression. Unlike the previous inspection technology, which can only measure the geometric parameters of fasteners to characterize the tightness, this system can directly estimate the tightening torque and the bolt clamping force. Experiments on WJ-8 fasteners showed a root mean square error of 9.272 N·m and 1.94 kN for the tightening torque and clamping force, demonstrating that the system is sufficiently precise to replace manual measurement and can substantially improve inspection efficiency while evaluating railway fastener looseness. Full article

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19 pages, 2311 KB

Open AccessArticle

Three-Dimensional Digital Zooming of Integral Imaging under Photon-Starved Conditions

by Gilsu Yeo and Myungjin Cho

Sensors 2023, 23(5), 2645; https://doi.org/10.3390/s23052645 - 28 Feb 2023

Cited by 2 | Viewed by 2192

Abstract

In this paper, we propose new three-dimensional (3D) visualization of objects at long distance under photon-starved conditions. In conventional three-dimensional image visualization techniques, the visual quality of three-dimensional images may be degraded because object images at long distances may have low resolution. Thus, [...] Read more.

In this paper, we propose new three-dimensional (3D) visualization of objects at long distance under photon-starved conditions. In conventional three-dimensional image visualization techniques, the visual quality of three-dimensional images may be degraded because object images at long distances may have low resolution. Thus, in our proposed method, we utilize digital zooming, which can crop and interpolate the region of interest from the image to improve the visual quality of three-dimensional images at long distances. Under photon-starved conditions, three-dimensional images at long distances may not be visualized due to the lack of the number of photons. Photon counting integral imaging can be used to solve this problem, but objects at long distance may still have a small number of photons. In our method, a three-dimensional image can be reconstructed, since photon counting integral imaging with digital zooming is used. In addition, to estimate a more accurate three-dimensional image at long distance under photon-starved conditions, in this paper, multiple observation photon counting integral imaging (i.e., N observation photon counting integral imaging) is used. To show the feasibility of our proposed method, we implement the optical experiments and calculate performance metrics, such as peak sidelobe ratio. Therefore, our method can improve the visualization of three-dimensional objects at long distances under photon-starved conditions. Full article

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15 pages, 5225 KB

Open AccessArticle

3D Scanner-Based Identification of Welding Defects—Clustering the Results of Point Cloud Alignment

by János Hegedűs-Kuti, József Szőlősi, Dániel Varga, János Abonyi, Mátyás Andó and Tamás Ruppert

Sensors 2023, 23(5), 2503; https://doi.org/10.3390/s23052503 - 23 Feb 2023

Cited by 13 | Viewed by 5093

Abstract

This paper describes a framework for detecting welding errors using 3D scanner data. The proposed approach employs density-based clustering to compare point clouds and identify deviations. The discovered clusters are then classified according to standard welding fault classes. Six welding deviations defined in [...] Read more.

This paper describes a framework for detecting welding errors using 3D scanner data. The proposed approach employs density-based clustering to compare point clouds and identify deviations. The discovered clusters are then classified according to standard welding fault classes. Six welding deviations defined in the ISO 5817:2014 standard were evaluated. All defects were represented through CAD models, and the method was able to detect five of these deviations. The results demonstrate that the errors can be effectively identified and grouped according to the location of the different points in the error clusters. However, the method cannot separate crack-related defects as a distinct cluster. Full article

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16 pages, 22548 KB

Open AccessArticle

Lensless Three-Dimensional Imaging under Photon-Starved Conditions

by Jae-Young Jang and Myungjin Cho

Sensors 2023, 23(4), 2336; https://doi.org/10.3390/s23042336 - 20 Feb 2023

Cited by 3 | Viewed by 2106

Abstract

In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack [...] Read more.

In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric. Full article

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33 pages, 9737 KB

Open AccessArticle

Multi-Class Classification and Multi-Output Regression of Three-Dimensional Objects Using Artificial Intelligence Applied to Digital Holographic Information

by Uma Mahesh R N and Anith Nelleri

Sensors 2023, 23(3), 1095; https://doi.org/10.3390/s23031095 - 17 Jan 2023

Cited by 1 | Viewed by 3677

Abstract

Digital holographically sensed 3D data processing, which is useful for AI-based vision, is demonstrated. Three prominent methods of learning from datasets such as sensed holograms, computationally retrieved intensity and phase from holograms forming concatenated intensity–phase (whole information) images, and phase-only images (depth information) [...] Read more.

Digital holographically sensed 3D data processing, which is useful for AI-based vision, is demonstrated. Three prominent methods of learning from datasets such as sensed holograms, computationally retrieved intensity and phase from holograms forming concatenated intensity–phase (whole information) images, and phase-only images (depth information) were utilized for the proposed multi-class classification and multi-output regression tasks of the chosen 3D objects in supervised learning. Each dataset comprised 2268 images obtained from the chosen eighteen 3D objects. The efficacy of our approaches was validated on experimentally generated digital holographic data then further quantified and compared using specific evaluation matrices. The machine learning classifiers had better AUC values for different classes on the holograms and whole information datasets compared to the CNN, whereas the CNN had a better performance on the phase-only image dataset compared to these classifiers. The MLP regressor was found to have a stable prediction in the test and validation sets with a fixed EV regression score of 0.00 compared to the CNN, the other regressors for holograms, and the phase-only image datasets, whereas the RF regressor showed a better performance in the validation set for the whole information dataset with a fixed EV regression score of 0.01 compared to the CNN and other regressors. Full article

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2022

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10 pages, 10341 KB

Open AccessCommunication

Multi-Scale Histogram-Based Probabilistic Deep Neural Network for Super-Resolution 3D LiDAR Imaging

by Miao Sun, Shenglong Zhuo and Patrick Yin Chiang

Sensors 2023, 23(1), 420; https://doi.org/10.3390/s23010420 - 30 Dec 2022

Cited by 2 | Viewed by 3475

Abstract

LiDAR (Light Detection and Ranging) imaging based on SPAD (Single-Photon Avalanche Diode) technology suffers from severe area penalty for large on-chip histogram peak detection circuits required by the high precision of measured depth values. In this work, a probabilistic estimation-based super-resolution neural network [...] Read more.

LiDAR (Light Detection and Ranging) imaging based on SPAD (Single-Photon Avalanche Diode) technology suffers from severe area penalty for large on-chip histogram peak detection circuits required by the high precision of measured depth values. In this work, a probabilistic estimation-based super-resolution neural network for SPAD imaging that firstly uses temporal multi-scale histograms as inputs is proposed. To reduce the area and cost of on-chip histogram computation, only part of the histogram hardware for calculating the reflected photons is implemented on a chip. On account of the distribution rule of returned photons, a probabilistic encoder as a part of the network is first proposed to solve the depth estimation problem of SPADs. By jointly using this neural network with a super-resolution network, 16× up-sampling depth estimation is realized using 32 × 32 multi-scale histogram outputs. Finally, the effectiveness of this neural network was verified in the laboratory with a 32 × 32 SPAD sensor system. Full article

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11 pages, 7961 KB

Open AccessCommunication

A Continuous Motion Shape-from-Focus Method for Geometry Measurement during 3D Printing

by Jona Gladines, Seppe Sels, Michael Hillen and Steve Vanlanduit

Sensors 2022, 22(24), 9805; https://doi.org/10.3390/s22249805 - 14 Dec 2022

Cited by 4 | Viewed by 2289

Abstract

In 3D printing, as in other manufacturing processes, there is a push for zero-defect manufacturing, mainly to avoid waste. To evaluate the quality of the printed parts during the printing process, an accurate 3D measurement method is required. By scanning the part during [...] Read more.

In 3D printing, as in other manufacturing processes, there is a push for zero-defect manufacturing, mainly to avoid waste. To evaluate the quality of the printed parts during the printing process, an accurate 3D measurement method is required. By scanning the part during the buildup, potential nonconformities to tolerances can be detected early on and the printing process could be adjusted to avoid scrapping the part. Out of many, shape-from-focus, is an accurate method for recovering 3D shapes from objects. However, the state-of-the-art implementation of the method requires the object to be stationary during a measurement. This does not reconcile with the nature of 3D printing, where continuous motion is required for the manufacturing process. This research presents a novel methodology that allows shape-from-focus to be used in a continuous scanning motion, thus making it possible to apply it to the 3D manufacturing process. By controlling the camera trigger and a tunable lens with synchronous signals, a stack of images can be created while the camera or the object is in motion. These images can be re-aligned and then used to create a 3D depth image. The impact on the quality of the 3D measurement was tested by analytically comparing the quality of a scan using the traditional stationary method and of the proposed method to a known reference. The results demonstrate a 1.22% degradation in the measurement error. Full article

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13 pages, 4823 KB

Open AccessArticle

Three-Dimensional Integral Imaging with Enhanced Lateral and Longitudinal Resolutions Using Multiple Pickup Positions

by Jiheon Lee and Myungjin Cho

Sensors 2022, 22(23), 9199; https://doi.org/10.3390/s22239199 - 26 Nov 2022

Cited by 5 | Viewed by 2124

Abstract

In this paper, we propose an enhancement of three-dimensional (3D) image visualization techniques by using different pickup plane reconstructions. In conventional 3D visualization techniques, synthetic aperture integral imaging (SAII) and volumetric computational reconstruction (VCR) can be utilized. However, due to the lack of [...] Read more.

In this paper, we propose an enhancement of three-dimensional (3D) image visualization techniques by using different pickup plane reconstructions. In conventional 3D visualization techniques, synthetic aperture integral imaging (SAII) and volumetric computational reconstruction (VCR) can be utilized. However, due to the lack of image information and shifting pixels, it may be difficult to obtain better lateral and longitudinal resolutions of 3D images. Thus, we propose a new elemental image acquisition and computational reconstruction to improve both the lateral and longitudinal resolutions of 3D objects. To prove the feasibility of our proposed method, we present the performance metrics, such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and peak-to-sidelobe ratio (PSR). Therefore, our method can improve both the lateral and longitudinal resolutions of 3D objects more than the conventional technique. Full article

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20 pages, 16022 KB

Open AccessArticle

3D Point Cloud Acquisition and Correction in Radioactive and Underwater Environments Using Industrial 3D Scanners

by Dongjun Hyun, Sungmoon Joo, Ikjune Kim and Jonghwan Lee

Sensors 2022, 22(23), 9053; https://doi.org/10.3390/s22239053 - 22 Nov 2022

Cited by 5 | Viewed by 2457

Abstract

This study proposes a method to acquire an accurate 3D point cloud in radioactive and underwater environments using industrial 3D scanners. Applications of robotic systems at nuclear facility dismantling require 3D imaging equipment for localization of target structures in radioactive and underwater environments. [...] Read more.

This study proposes a method to acquire an accurate 3D point cloud in radioactive and underwater environments using industrial 3D scanners. Applications of robotic systems at nuclear facility dismantling require 3D imaging equipment for localization of target structures in radioactive and underwater environments. The use of industrial 3D scanners may be a better option than developing prototypes for researchers with basic knowledge. However, such industrial 3D scanners are designed to operate in normal environments and cannot be used in radioactive and underwater environments. Modifications to environmental obstacles also suffer from hidden technical details of industrial 3D scanners. This study shows how 3D imaging equipment based on the industrial 3D scanner satisfies the requirements of the remote dismantling system, using a robotic system despite insufficient environmental resistance and hidden technical details of industrial 3D scanners. A housing unit is designed for waterproofing and radiation protection using windows, mirrors and shielding. Shielding protects the industrial 3D scanner from radiation damage. Mirrors reflect the light required for 3D scanning because shielding blocks the light. Windows in the waterproof housing also transmit the light required for 3D scanning with the industrial 3D scanner. The basic shielding thickness calculation method through the experimental method is described, including the analysis of the experimental results. The method for refraction correction through refraction modeling, measurement experiments and parameter studies are described. The developed 3D imaging equipment successfully satisfies the requirements of the remote dismantling system: waterproof, radiation resistance of 1 kGy and positional accuracy within 1 mm. The proposed method is expected to provide researchers with an easy approach to 3D scanning in radioactive and underwater environments. Full article

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15 pages, 3609 KB

Open AccessArticle

Calibration of a Catadioptric System and 3D Reconstruction Based on Surface Structured Light

by Zhenghai Lu, Yaowen Lv, Zhiqing Ai, Ke Suo, Xuanrui Gong and Yuxuan Wang

Sensors 2022, 22(19), 7385; https://doi.org/10.3390/s22197385 - 28 Sep 2022

Cited by 3 | Viewed by 2675

Abstract

In response to the problem of the small field of vision in 3D reconstruction, a 3D reconstruction system based on a catadioptric camera and projector was built by introducing a traditional camera to calibrate the catadioptric camera and projector system. Firstly, the intrinsic [...] Read more.

In response to the problem of the small field of vision in 3D reconstruction, a 3D reconstruction system based on a catadioptric camera and projector was built by introducing a traditional camera to calibrate the catadioptric camera and projector system. Firstly, the intrinsic parameters of the camera and the traditional camera are calibrated separately. Then, the calibration of the projection system is accomplished by the traditional camera. Secondly, the coordinate system is introduced to calculate, respectively, the position of the catadioptric camera and projector in the coordinate system, and the position relationship between the coordinate systems of the catadioptric camera and the projector is obtained. Finally, the projector is used to project the structured light fringe to realize the reconstruction using a catadioptric camera. The experimental results show that the reconstruction error is 0.75 mm and the relative error is 0.0068 for a target of about 1 m. The calibration method and reconstruction method proposed in this paper can guarantee the ideal geometric reconstruction accuracy. Full article

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40 pages, 11261 KB

Open AccessArticle

An Experimental Assessment of Depth Estimation in Transparent and Translucent Scenes for Intel RealSense D415, SR305 and L515

by Eva Curto and Helder Araujo

Sensors 2022, 22(19), 7378; https://doi.org/10.3390/s22197378 - 28 Sep 2022

Cited by 15 | Viewed by 7371

Abstract

RGB-D cameras have become common in many research fields since these inexpensive devices provide dense 3D information from the observed scene. Over the past few years, the RealSense™ range from Intel^® has introduced new, cost-effective RGB-D sensors with different technologies, more sophisticated [...] Read more.

RGB-D cameras have become common in many research fields since these inexpensive devices provide dense 3D information from the observed scene. Over the past few years, the RealSense™ range from Intel^® has introduced new, cost-effective RGB-D sensors with different technologies, more sophisticated in both hardware and software. Models D415, SR305, and L515 are examples of successful cameras launched by Intel^® RealSense™ between 2018 and 2020. These three cameras are different since they have distinct operating principles. Then, their behavior concerning depth estimation while in the presence of many error sources will also be specific. For instance, semi-transparent and scattering media are expected error sources for an RGB-D sensor. The main new contribution of this paper is a full evaluation and comparison between the three Intel RealSense cameras in scenarios with transparency and translucency. We propose an experimental setup involving an aquarium and liquids. The evaluation, based on repeatability/precision and statistical distribution of the acquired depth, allows us to compare the three cameras and conclude that Intel RealSense D415 has overall the best behavior namely in what concerns the statistical variability (also known as precision or repeatability) and also in what concerns valid measurements. Full article

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16 pages, 11462 KB

Open AccessArticle

Physics-Based Simulation of Soft-Body Deformation Using RGB-D Data

by Daeun Kang, Jaeseok Moon, Saeyoung Yang, Taesoo Kwon and Yejin Kim

Sensors 2022, 22(19), 7225; https://doi.org/10.3390/s22197225 - 23 Sep 2022

Cited by 2 | Viewed by 5028

Abstract

Providing real-time interaction in an immersive environment has drawn considerable attention in the virtual training fields. Physics-based simulations are suitable for such environments; however, they require the definition and adjustment of coefficients that determine material properties, making the methods more complex and time-consuming. [...] Read more.

Providing real-time interaction in an immersive environment has drawn considerable attention in the virtual training fields. Physics-based simulations are suitable for such environments; however, they require the definition and adjustment of coefficients that determine material properties, making the methods more complex and time-consuming. In this paper, we introduce a novel approach to simulating the soft-body deformation of an observed object. Using an off-the-shelf RGB-D sensor, the proposed approach tracks an object’s movement and simulates its deformation in an iterative manner. Polygonal models with different resolutions are used to improve the simulation speed and visual quality. During the simulation process, a low-resolution model is used for surface deformation using neighboring feature points detected from the sensor, and a volumetric model is added for internal force estimation. To visualize the observed model in detail, the deformed and low-resolution model is mapped to a high-resolution model using mean value coordinate interpolation. To handle topological deformations, such as cutting or tearing, a part intersected by a cutting tool is recognized by the sensor and responds to external forces. As shown in the experimental results, our approach generates convincing deformations of observed objects in real time. Full article

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17 pages, 5108 KB

Open AccessArticle

Millimeter-Wave Imaging System Based on Direct-Conversion Focal-Plane Array Receiver

by Sergey Korolyov, Aleksandr Goryunov, Ivan Illarionov, Vladimir Parshin and Petr Zemlyanukha

Sensors 2022, 22(19), 7132; https://doi.org/10.3390/s22197132 - 20 Sep 2022

Cited by 4 | Viewed by 3441

Abstract

A new approach to millimeter-wave imaging was suggested and experimentally studied. This approach can be considered as the evolution of the well-established focal-plane array (FPA) millimeter-wave imaging. The significant difference is the use of a direct-conversion array receiver, instead of the direct-detection array [...] Read more.

A new approach to millimeter-wave imaging was suggested and experimentally studied. This approach can be considered as the evolution of the well-established focal-plane array (FPA) millimeter-wave imaging. The significant difference is the use of a direct-conversion array receiver, instead of the direct-detection array receiver, along with the frequency-modulated continuous-wave (FMCW) radar technique. The sensitivity of the direct-conversion receiver is several orders higher than the sensitivity of the direct-detection one, which allows us to increase the maximum imaging range by more than one order of magnitude. The additional advantage of the direct-conversion technique is the opportunity to obtain information about the range to an object. The realization of the direct-conversion FPA imaging system was made possible due to original sensitive simple-designed receiving elements based on low-barrier Mott diodes. The suggested imaging method’s main characteristics, which include the achievable angular and range resolution and the achievable maximum imaging range, were studied. A maximum range of up to 100 m was experimentally determined. A 94 GHz 8 × 8 imaging system was developed for demonstration purposes and studied in detail. The suggested technique is assumed to be useful for creating a long-range millimeter-wave camera, in particular, for robotic systems that operate in poor environmental conditions. Full article

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17 pages, 6082 KB

Open AccessArticle

Research on Multi-View 3D Reconstruction Technology Based on SFM

by Lei Gao, Yingbao Zhao, Jingchang Han and Huixian Liu

Sensors 2022, 22(12), 4366; https://doi.org/10.3390/s22124366 - 9 Jun 2022

Cited by 40 | Viewed by 6901

Abstract

Multi-view 3D reconstruction technology is used to restore a 3D model of practical value or required objects from a group of images. This paper designs and implements a set of multi-view 3D reconstruction technology, adopts the fusion method of SIFT and SURF feature-point [...] Read more.

Multi-view 3D reconstruction technology is used to restore a 3D model of practical value or required objects from a group of images. This paper designs and implements a set of multi-view 3D reconstruction technology, adopts the fusion method of SIFT and SURF feature-point extraction results, increases the number of feature points, adds proportional constraints to improve the robustness of feature-point matching, and uses RANSAC to eliminate false matching. In the sparse reconstruction stage, the traditional incremental SFM algorithm takes a long time, but the accuracy is high; the traditional global SFM algorithm is fast, but its accuracy is low; aiming at the disadvantages of traditional SFM algorithm, this paper proposes a hybrid SFM algorithm, which avoids the problem of the long time consumption of incremental SFM and the problem of the low precision and poor robustness of global SFM; finally, the MVS algorithm of depth-map fusion is used to complete the dense reconstruction of objects, and the related algorithms are used to complete the surface reconstruction, which makes the reconstruction model more realistic. Full article

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13 pages, 726 KB

Open AccessArticle

Deep Neural Network for Point Sets Based on Local Feature Integration

by Hao Chu, Zhenquan He, Shangdong Liu, Chuanwen Liu, Jiyuan Yang and Fei Wang

Sensors 2022, 22(9), 3209; https://doi.org/10.3390/s22093209 - 22 Apr 2022

Cited by 3 | Viewed by 2930

Abstract

The research of object classification and part segmentation is a hot topic in computer vision, robotics, and virtual reality. With the emergence of depth cameras, point clouds have become easier to collect and increasingly important because of their simple and unified structures. Recently, [...] Read more.

The research of object classification and part segmentation is a hot topic in computer vision, robotics, and virtual reality. With the emergence of depth cameras, point clouds have become easier to collect and increasingly important because of their simple and unified structures. Recently, a considerable number of studies have been carried out about deep learning on 3D point clouds. However, data captured directly by sensors from the real-world often encounters severe incomplete sampling problems. The classical network is able to learn deep point set features efficiently, but it is not robust enough when the method suffers from the lack of point clouds. In this work, a novel and general network was proposed, whose effect does not depend on a large amount of point cloud input data. The mutual learning of neighboring points and the fusion between high and low feature layers can better promote the integration of local features so that the network can be more robust. The specific experiments were conducted on the ScanNet and Modelnet40 datasets with 84.5% and 92.8% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks, and has good local feature integration ability. Particularly, it can still maintain 87.4% accuracy when the number of input points is further reduced to 128. The model proposed has bridged the gap between classical networks and point cloud processing. Full article

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