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Special Issue "Depth Sensors and 3D Vision"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 August 2018

Special Issue Editor

Guest Editor
Prof. Roberto Vezzani

AImageLab, Dipartimento di Ingegneria "Enzo Ferrari", University of Modena and Reggio Emilia, Modena, Italy
Website | E-Mail
Interests: computer vision; image processing; machine vision; pattern recognition; surveillance; people behavior understanding; human-computer interaction; depth sensors; 3D vision

Special Issue Information

Dear Colleagues,

The recent diffusion of inexpensive RGB-D sensors has encouraged the computer vision community to explore new solutions based on depth images. Depth information provides a significant contribution to solve or simplify several challenging tasks, such as shape analysis and classification, scene reconstruction, object segmentation, people detection, and body part recognition. The intrinsic metric information as well as the ability to handle texture and illumination variations of objects and scenes are only two of the advantages with respect to pure RGB images.

For example, hardware and software technologies included in the Microsoft Kinect framework allow an easy estimation of the 3D positions of skeleton joints, providing a new compact and expressive representation of the human body.

Although the Kinect failed as a gaming-first device, it has been a launch pad for the spread of depth sensors and, contextually, 3D vision. From a hardware perspective, several stereo, structured IR light, and ToF sensors have appeared on the market, and are studied by the scientific community. At the same time, computer vision and machine learning communities have proposed new solutions to process depth data, individually or fused with other information such as RGB images.

This Special Issue seeks innovative work to explore new hardware and software solutions for the generation and analysis of depth data, including representation models, machine learning approaches, datasets, and benchmarks.

The particular topics of interest include, but are not limited to:

  • Depth acquisition techniques
  • Depth data processing
  • Analysis of depth data
  • Fusion of depth data with other modalities
  • From and to depth domain translation
  • 3D scene reconstruction
  • 3D shape modeling and retrieval
  • 3D object recognition
  • 3D biometrics
  • 3D imaging for cultural heritage applications
  • Point cloud modelling and processing
  • Human action recognition on depth data
  • Biomedical applications of depth data
  • Other applications of depth data analysis
  • Depth datasets and benchmarks
  • Depth data visualization

Prof. Roberto Vezzani
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Depth sensors
  • 3D vision
  • Depth data generation
  • Depth data analysis
  • Depth datasets

Published Papers (11 papers)

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Research

Open AccessArticle Efficient 3D Objects Recognition Using Multifoveated Point Clouds
Sensors 2018, 18(7), 2302; https://doi.org/10.3390/s18072302
Received: 16 June 2018 / Revised: 10 July 2018 / Accepted: 11 July 2018 / Published: 16 July 2018
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Abstract
Technological innovations in the hardware of RGB-D sensors have allowed the acquisition of 3D point clouds in real time. Consequently, various applications have arisen related to the 3D world, which are receiving increasing attention from researchers. Nevertheless, one of the main problems that
[...] Read more.
Technological innovations in the hardware of RGB-D sensors have allowed the acquisition of 3D point clouds in real time. Consequently, various applications have arisen related to the 3D world, which are receiving increasing attention from researchers. Nevertheless, one of the main problems that remains is the demand for computationally intensive processing that required optimized approaches to deal with 3D vision modeling, especially when it is necessary to perform tasks in real time. A previously proposed multi-resolution 3D model known as foveated point clouds can be a possible solution to this problem. Nevertheless, this is a model limited to a single foveated structure with context dependent mobility. In this work, we propose a new solution for data reduction and feature detection using multifoveation in the point cloud. Nonetheless, the application of several foveated structures results in a considerable increase of processing since there are intersections between regions of distinct structures, which are processed multiple times. Towards solving this problem, the current proposal brings an approach that avoids the processing of redundant regions, which results in even more reduced processing time. Such approach can be used to identify objects in 3D point clouds, one of the key tasks for real-time applications as robotics vision, with efficient synchronization allowing the validation of the model and verification of its applicability in the context of computer vision. Experimental results demonstrate a performance gain of at least 27.21% in processing time while retaining the main features of the original, and maintaining the recognition quality rate in comparison with state-of-the-art 3D object recognition methods. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle A Miniature Binocular Endoscope with Local Feature Matching and Stereo Matching for 3D Measurement and 3D Reconstruction
Sensors 2018, 18(7), 2243; https://doi.org/10.3390/s18072243
Received: 17 May 2018 / Revised: 17 June 2018 / Accepted: 28 June 2018 / Published: 12 July 2018
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Abstract
As the traditional single camera endoscope can only provide clear images without 3D measurement and 3D reconstruction, a miniature binocular endoscope based on the principle of binocular stereoscopic vision to implement 3D measurement and 3D reconstruction in tight and restricted spaces is presented.
[...] Read more.
As the traditional single camera endoscope can only provide clear images without 3D measurement and 3D reconstruction, a miniature binocular endoscope based on the principle of binocular stereoscopic vision to implement 3D measurement and 3D reconstruction in tight and restricted spaces is presented. In order to realize the exact matching of points of interest in the left and right images, a novel construction method of the weighted orthogonal-symmetric local binary pattern (WOS-LBP) descriptor is presented. Then a stereo matching algorithm based on Gaussian-weighted AD-Census transform and improved cross-based adaptive regions is studied to realize 3D reconstruction for real scenes. In the algorithm, we adjust determination criterions of adaptive regions for edge and discontinuous areas in particular and as well extract mismatched pixels caused by occlusion through image entropy and region-growing algorithm. This paper develops a binocular endoscope with an external diameter of 3.17 mm and the above algorithms are applied in it. The endoscope contains two CMOS cameras and four fiber optics for illumination. Three conclusions are drawn from experiments: (1) the proposed descriptor has good rotation invariance, distinctiveness and robustness to light change as well as noises; (2) the proposed stereo matching algorithm has a mean relative error of 8.48% for Middlebury standard pairs of images and compared with several classical stereo matching algorithms, our algorithm performs better in edge and discontinuous areas; (3) the mean relative error of length measurement is 3.22%, and the endoscope can be utilized to measure and reconstruct real scenes effectively. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Accurate Calibration of Multi-LiDAR-Multi-Camera Systems
Sensors 2018, 18(7), 2139; https://doi.org/10.3390/s18072139
Received: 26 May 2018 / Revised: 25 June 2018 / Accepted: 29 June 2018 / Published: 3 July 2018
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Abstract
As autonomous driving attracts more and more attention these days, the algorithms and sensors used for machine perception become popular in research, as well. This paper investigates the extrinsic calibration of two frequently-applied sensors: the camera and Light Detection and Ranging (LiDAR). The
[...] Read more.
As autonomous driving attracts more and more attention these days, the algorithms and sensors used for machine perception become popular in research, as well. This paper investigates the extrinsic calibration of two frequently-applied sensors: the camera and Light Detection and Ranging (LiDAR). The calibration can be done with the help of ordinary boxes. It contains an iterative refinement step, which is proven to converge to the box in the LiDAR point cloud, and can be used for system calibration containing multiple LiDARs and cameras. For that purpose, a bundle adjustment-like minimization is also presented. The accuracy of the method is evaluated on both synthetic and real-world data, outperforming the state-of-the-art techniques. The method is general in the sense that it is both LiDAR and camera-type independent, and only the intrinsic camera parameters have to be known. Finally, a method for determining the 2D bounding box of the car chassis from LiDAR point clouds is also presented in order to determine the car body border with respect to the calibrated sensors. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Human Part Segmentation in Depth Images with Annotated Part Positions
Sensors 2018, 18(6), 1900; https://doi.org/10.3390/s18061900
Received: 2 May 2018 / Revised: 31 May 2018 / Accepted: 8 June 2018 / Published: 11 June 2018
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Abstract
We present a method of segmenting human parts in depth images, when provided the image positions of the body parts. The goal is to facilitate per-pixel labelling of large datasets of human images, which are used for training and testing algorithms for pose
[...] Read more.
We present a method of segmenting human parts in depth images, when provided the image positions of the body parts. The goal is to facilitate per-pixel labelling of large datasets of human images, which are used for training and testing algorithms for pose estimation and automatic segmentation. A common technique in image segmentation is to represent an image as a two-dimensional grid graph, with one node for each pixel and edges between neighbouring pixels. We introduce a graph with distinct layers of nodes to model occlusion of the body by the arms. Once the graph is constructed, the annotated part positions are used as seeds for a standard interactive segmentation algorithm. Our method is evaluated on two public datasets containing depth images of humans from a frontal view. It produces a mean per-class accuracy of 93.55% on the first dataset, compared to 87.91% (random forest and graph cuts) and 90.31% (random forest and Markov random field). It also achieves a per-class accuracy of 90.60% on the second dataset. Future work can experiment with various methods for creating the graph layers to accurately model occlusion. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Pulse Based Time-of-Flight Range Sensing
Sensors 2018, 18(6), 1679; https://doi.org/10.3390/s18061679
Received: 3 April 2018 / Revised: 10 May 2018 / Accepted: 21 May 2018 / Published: 23 May 2018
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Abstract
Pulse-based Time-of-Flight (PB-ToF) cameras are an attractive alternative range imaging approach, compared to the widely commercialized Amplitude Modulated Continuous-Wave Time-of-Flight (AMCW-ToF) approach. This paper presents an in-depth evaluation of a PB-ToF camera prototype based on the Hamamatsu area sensor S11963-01CR. We evaluate different
[...] Read more.
Pulse-based Time-of-Flight (PB-ToF) cameras are an attractive alternative range imaging approach, compared to the widely commercialized Amplitude Modulated Continuous-Wave Time-of-Flight (AMCW-ToF) approach. This paper presents an in-depth evaluation of a PB-ToF camera prototype based on the Hamamatsu area sensor S11963-01CR. We evaluate different ToF-related effects, i.e., temperature drift, systematic error, depth inhomogeneity, multi-path effects, and motion artefacts. Furthermore, we evaluate the systematic error of the system in more detail, and introduce novel concepts to improve the quality of range measurements by modifying the mode of operation of the PB-ToF camera. Finally, we describe the means of measuring the gate response of the PB-ToF sensor and using this information for PB-ToF sensor simulation. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Extrinsic Calibration of a Laser Galvanometric Setup and a Range Camera
Sensors 2018, 18(5), 1478; https://doi.org/10.3390/s18051478
Received: 13 March 2018 / Revised: 21 April 2018 / Accepted: 21 April 2018 / Published: 8 May 2018
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Abstract
Currently, galvanometric scanning systems (like the one used in a scanning laser Doppler vibrometer) rely on a planar calibration procedure between a two-dimensional (2D) camera and the laser galvanometric scanning system to automatically aim a laser beam at a particular point on an
[...] Read more.
Currently, galvanometric scanning systems (like the one used in a scanning laser Doppler vibrometer) rely on a planar calibration procedure between a two-dimensional (2D) camera and the laser galvanometric scanning system to automatically aim a laser beam at a particular point on an object. In the case of nonplanar or moving objects, this calibration is not sufficiently accurate anymore. In this work, a three-dimensional (3D) calibration procedure that uses a 3D range sensor is proposed. The 3D calibration is valid for all types of objects and retains its accuracy when objects are moved between subsequent measurement campaigns. The proposed 3D calibration uses a Non-Perspective-n-Point (NPnP) problem solution. The 3D range sensor is used to calculate the position of the object under test relative to the laser galvanometric system. With this extrinsic calibration, the laser galvanometric scanning system can automatically aim a laser beam to this object. In experiments, the mean accuracy of aiming the laser beam on an object is below 10 mm for 95% of the measurements. This achieved accuracy is mainly determined by the accuracy and resolution of the 3D range sensor. The new calibration method is significantly better than the original 2D calibration method, which in our setup achieves errors below 68 mm for 95% of the measurements. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Geometric Integration of Hybrid Correspondences for RGB-D Unidirectional Tracking
Sensors 2018, 18(5), 1385; https://doi.org/10.3390/s18051385
Received: 9 April 2018 / Revised: 23 April 2018 / Accepted: 27 April 2018 / Published: 1 May 2018
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Abstract
Traditionally, visual-based RGB-D SLAM systems only use correspondences with valid depth values for camera tracking, thus ignoring the regions without 3D information. Due to the strict limitation on measurement distance and view angle, such systems adopt only short-range constraints which may introduce larger
[...] Read more.
Traditionally, visual-based RGB-D SLAM systems only use correspondences with valid depth values for camera tracking, thus ignoring the regions without 3D information. Due to the strict limitation on measurement distance and view angle, such systems adopt only short-range constraints which may introduce larger drift errors during long-distance unidirectional tracking. In this paper, we propose a novel geometric integration method that makes use of both 2D and 3D correspondences for RGB-D tracking. Our method handles the problem by exploring visual features both when depth information is available and when it is unknown. The system comprises two parts: coarse pose tracking with 3D correspondences, and geometric integration with hybrid correspondences. First, the coarse pose tracking generates the initial camera pose using 3D correspondences with frame-by-frame registration. The initial camera poses are then used as inputs for the geometric integration model, along with 3D correspondences, 2D-3D correspondences and 2D correspondences identified from frame pairs. The initial 3D location of the correspondence is determined in two ways, from depth image and by using the initial poses to triangulate. The model improves the camera poses and decreases drift error during long-distance RGB-D tracking iteratively. Experiments were conducted using data sequences collected by commercial Structure Sensors. The results verify that the geometric integration of hybrid correspondences effectively decreases the drift error and improves mapping accuracy. Furthermore, the model enables a comparative and synergistic use of datasets, including both 2D and 3D features. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model
Sensors 2018, 18(5), 1318; https://doi.org/10.3390/s18051318
Received: 30 March 2018 / Revised: 19 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
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Abstract
This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a
[...] Read more.
This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a continuous pairwise Conditional Random Field (CRF) model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle In Situ 3D Monitoring of Geometric Signatures in the Powder-Bed-Fusion Additive Manufacturing Process via Vision Sensing Methods
Sensors 2018, 18(4), 1180; https://doi.org/10.3390/s18041180
Received: 1 March 2018 / Revised: 29 March 2018 / Accepted: 10 April 2018 / Published: 12 April 2018
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Abstract
Lack of monitoring of the in situ process signatures is one of the challenges that has been restricting the improvement of Powder-Bed-Fusion Additive Manufacturing (PBF AM). Among various process signatures, the monitoring of the geometric signatures is of high importance. This paper presents
[...] Read more.
Lack of monitoring of the in situ process signatures is one of the challenges that has been restricting the improvement of Powder-Bed-Fusion Additive Manufacturing (PBF AM). Among various process signatures, the monitoring of the geometric signatures is of high importance. This paper presents the use of vision sensing methods as a non-destructive in situ 3D measurement technique to monitor two main categories of geometric signatures: 3D surface topography and 3D contour data of the fusion area. To increase the efficiency and accuracy, an enhanced phase measuring profilometry (EPMP) is proposed to monitor the 3D surface topography of the powder bed and the fusion area reliably and rapidly. A slice model assisted contour detection method is developed to extract the contours of fusion area. The performance of the techniques is demonstrated with some selected measurements. Experimental results indicate that the proposed method can reveal irregularities caused by various defects and inspect the contour accuracy and surface quality. It holds the potential to be a powerful in situ 3D monitoring tool for manufacturing process optimization, close-loop control, and data visualization. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Three-Dimensional Registration for Handheld Profiling Systems Based on Multiple Shot Structured Light
Sensors 2018, 18(4), 1146; https://doi.org/10.3390/s18041146
Received: 15 February 2018 / Revised: 27 March 2018 / Accepted: 3 April 2018 / Published: 9 April 2018
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Abstract
In this article, a multi-view registration approach for the 3D handheld profiling system based on the multiple shot structured light technique is proposed. The multi-view registration approach is categorized into coarse registration and point cloud refinement using the iterative closest point (ICP) algorithm.
[...] Read more.
In this article, a multi-view registration approach for the 3D handheld profiling system based on the multiple shot structured light technique is proposed. The multi-view registration approach is categorized into coarse registration and point cloud refinement using the iterative closest point (ICP) algorithm. Coarse registration of multiple point clouds was performed using relative orientation and translation parameters estimated via homography-based visual navigation. The proposed system was evaluated using an artificial human skull and a paper box object. For the quantitative evaluation of the accuracy of a single 3D scan, a paper box was reconstructed, and the mean errors in its height and breadth were found to be 9.4 μm and 23 μm, respectively. A comprehensive quantitative evaluation and comparison of proposed algorithm was performed with other variants of ICP. The root mean square error for the ICP algorithm to register a pair of point clouds of the skull object was also found to be less than 1 mm. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Open AccessArticle Accurate Object Pose Estimation Using Depth Only
Sensors 2018, 18(4), 1045; https://doi.org/10.3390/s18041045
Received: 14 February 2018 / Revised: 7 March 2018 / Accepted: 28 March 2018 / Published: 30 March 2018
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Abstract
Object recognition and pose estimation is an important task in computer vision. A pose estimation algorithm using only depth information is proposed in this paper. Foreground and background points are distinguished based on their relative positions with boundaries. Model templates are selected using
[...] Read more.
Object recognition and pose estimation is an important task in computer vision. A pose estimation algorithm using only depth information is proposed in this paper. Foreground and background points are distinguished based on their relative positions with boundaries. Model templates are selected using synthetic scenes to make up for the point pair feature algorithm. An accurate and fast pose verification method is introduced to select result poses from thousands of poses. Our algorithm is evaluated against a large number of scenes and proved to be more accurate than algorithms using both color information and depth information. Full article
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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