Large-scale 3D-scanned point clouds enable the accurate and easy recording of complex 3D objects in the real world. The acquired point clouds often describe both the surficial and internal 3D structure of the scanned objects. The recently proposed ed...
Automobile datasets for 3D object detection are typically obtained using expensive high-resolution rotating LiDAR with 64 or more channels (Chs). However, the research budget may be limited such that only a low-resolution LiDAR of 32-Ch or lower can...
Three-dimensional (3D) point clouds have a wide range of applications in the field of 3D vision. The quality of the acquired point cloud data considerably impacts the subsequent work of point cloud processing. Due to the sparsity and irregularity of...
With the rapid development of 3D scanners, the cultural heritage artifacts can be stored as a point cloud and displayed through the Internet. However, due to natural and human factors, many cultural relics had some surface damage when excavated. As a...
Recently, research using point clouds has been increasing with the development of 3D scanner technology. According to this trend, the demand for high-quality point clouds is increasing, but there is still a problem with the high cost of obtaining hig...
Since 3D sensors became popular, imaged depth data are easier to obtain in the consumer sector. In applications such as defect localization on industrial objects or mass/volume estimation, precise depth data is important and, thus, benefits from the...
The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses a...
This paper presents a self-tuned two-stage framework for point cloud reconstruction. A parameter-free denoising module (TPDn) automatically selects thresholds through polynomial model fitting to remove noise and outliers without manual tuning. The de...
The upsampling of point clouds is a common task to increase the expressiveness and richness of the details. The quality of upsampled point clouds is crucial for downstream tasks, such as mesh reconstruction. With the rapid development of deep learnin...
X-ray diffraction can non-destructively reveal microstructure information, including stress distribution on thermal coatings of aeroengine blades. In order to accurately pinpoint the detection position and precisely set the measurement geometry, a 3D...
In this work, novel denoising and super resolution (SR) approaches for magnetic resonance (MR) images are addressed, and are integrated in a unified framework, which do not require example low resolution (LR)/high resolution (HR)/cross-modality/noise...
Point clouds are widely used in remote sensing applications, e.g., 3D object classification, semantic segmentation, and building reconstruction. Generating dense and uniformly distributed point clouds from low-density ones is beneficial to 3D point c...
In this paper, a set of techniques used for downsampling and upsampling of 2D images is analyzed on various image datasets. The comparison takes into account a significant number of interpolation kernels, their parameters, and their algebraical form,...
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...
Transformer-based methods have demonstrated impressive performance in image super-resolution tasks. However, when applied to large-scale Earth observation images, the existing transformers encounter two significant challenges: (1) insufficient consid...
Depth estimation algorithms are widely applied in various fields, including 3D reconstruction, autonomous driving, and industrial robotics. Monocular self-supervised algorithms for depth prediction offer a cost-effective alternative to acquiring dept...
Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is...
In recent years, the development of super-resolution (SR) algorithms based on convolutional neural networks has become an important topic in enhancing the resolution of multi-channel remote sensing images. However, most of the existing SR models suff...
Human Pose Estimation (HPE) remains challenging due to scale variation, occlusion, and high computational costs. Standard methods often struggle to capture detailed spatial information when keypoints are obscured, and they typically rely on computati...
In this paper, we propose a novel model to extract highly precise depth maps from missing viewpoints, especially for generating holographic 3D content. These depth maps are essential elements for phase extraction, which is required for the synthesis...
In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts...
Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors. Recently, brain tumor segmentation methods based on the symmetric U-Net archite...
Remote sensing image (RSI) super-resolution plays a critical role in improving image details and reducing costs associated with physical imaging devices. However, existing super-resolution methods are not applicable to resource-constrained edge devic...
Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. To address the issues of sparse feature loss for small targets during the down-sampling pha...
Indoor-scene semantic segmentation is of great significance to indoor navigation, high-precision map creation, route planning, etc. However, incorporating RGB and HHA images for indoor-scene semantic segmentation is a promising yet challenging task,...
Steel, a fundamental material in modern industry, is widely used across manufacturing, construction, and energy sectors. Steel surface defects exhibit characteristics such as multiple classes, multi-scale features, small detection targets, and low-co...
To improve acquisition efficiency and achieve super high-resolution reconstruction, a computational integral imaging reconstruction (CIIR) method based on the generative adversarial network (GAN) network is proposed. Firstly, a sparse camera array is...
The advancement of hyperspectral image super-resolution (HSI-SR) has been significantly propelled by deep learning techniques. However, current methods predominantly rely on 2D or 3D convolutional networks, which are inherently local and thus limited...
Owing to imperfect scans, occlusions, low reflectance of the scanned surface, and packet loss, there may be several incomplete regions in the 3D point cloud dataset. These missing regions can degrade the performance of recognition, classification, se...
In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the individual deep learning model components in performing prostate segmentation. We found that for upsampling, the combination of interpolation and co...
Prediction Error Ordering (PEO) can integrate the encoding gains of both prediction and sorting by replacing the PVO (Pixel Value Ordering) pixel blocks with prediction-error blocks before sorting. It remains an open topic to construct a consistent,...
Current Multi-View Stereo (MVS) algorithms are tools for high-quality 3D model reconstruction, strongly depending on image spatial resolution. In this context, the combination of image Super-Resolution (SR) with image-based 3D reconstruction is turni...
The increasing reliance on 3D scanning and meshless methods highlights the need for algorithms optimized for point-cloud geometry representations in CAE simulations. While voxel-based binning methods are simple, they often compromise geometry and top...
Most current point cloud super-resolution reconstruction requires huge calculations and has low accuracy when facing large outdoor scenes; a Dense Feature Pyramid Network (DenseFPNet) is proposed for the feature-level fusion of images with low-resolu...
The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance...
In the industrial field, the 3D target detection algorithm PointPillars has gained popularity. Improving target detection accuracy while maintaining high efficiency has been a significant challenge. To address the issue of low target detection accura...
This paper proposes a framework that enables the online generation of virtual point clouds relying only on previous camera and point clouds and current camera measurements. The continuous usage of the pipeline generating virtual LIDAR measurements ma...
Radar echo extrapolation has been widely developed in previous studies for precipitation and storm nowcasting. However, most studies have focused on two-dimensional radar images, and extrapolation of multi-altitude radar images, which can provide mor...
To accurately estimate the 6D pose of objects, most methods employ a two-stage algorithm. While such two-stage algorithms achieve high accuracy, they are often slow. Additionally, many approaches utilize encoding–decoding to obtain the 6D pose,...
Improving the accuracy and real-time performance of strawberry recognition and localization algorithms remains a major challenge in intelligent harvesting. To address this, this study presents an integrated approach for strawberry maturity detection...
With the rapid advancements in AI technology, UAV-based inspection has become a mainstream method for intelligent maintenance of PV power stations. To address limitations in accuracy and data acquisition, this paper presents a defect detection algori...
One-dimensional (1D) nanowires represent a critical class of nanomaterials with extensive applications in biosensing, biomedicine, bioelectronics, and energy harvesting. In materials science, accurately extracting their morphological and structural f...
In the field of geoinformation science, multiview, image-based 3D city modeling has developed rapidly, and image depth estimation is an important step in it. To address the problems of the poor adaptability of training models of existing neural netwo...
Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural networks (CNNs) have demonstrated exceptional performance in compu...
Multi-modality three-dimensional (3D) object detection is a crucial technology for the safe and effective operation of environment perception systems in autonomous driving. In this study, we propose a method called context clustering-based radar and...
Recent research on single-image super-resolution (SISR) using deep convolutional neural networks has made a breakthrough and achieved tremendous performance. Despite their significant progress, numerous convolutional neural networks (CNN) are limited...
Malignant lesions are a huge threat to human health and have a high mortality rate. Locating the contour of organs is a preparation step, and it helps doctors diagnose correctly. Therefore, there is an urgent clinical need for a segmentation model sp...
Although the development of high-resolution remote sensing satellite technology has made it possible to reconstruct the 3D structure of object-level features using satellite imagery, the results from a single reconstruction are often insufficient to...
Environmental perception is crucial for safe autonomous driving, enabling accurate analysis of the vehicle’s surroundings. While 3D LiDAR is traditionally used for 3D environment reconstruction, its high cost and complexity present challenges....
Accurate depth estimation is crucial for many fields, including robotics, navigation, and medical imaging. However, conventional depth sensors often produce low-resolution (LR) depth maps, making detailed scene perception challenging. To address this...
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