3D hand pose estimation has achieved remarkable progress in human computer interaction and computer vision; however, real-world hand point clouds often suffer from structural distortions such as partial occlusions, sensor noise, and environmental int...
Accurate classification of three-dimensional (3D) point clouds in real-world environments is often impeded by sensor noise, occlusions, and incomplete data. To overcome these challenges, we propose SMCNet, a robust multimodal framework for 3D point c...
Three-dimensional point cloud recognition is a very fundamental work in fields such as autonomous driving and face recognition. However, in real industrial scenarios, input point cloud data are often accompanied by factors such as occlusion, rotation...
Point clouds obtained with 3D scanners in realistic scenes inevitably contain corruption, including noise and outliers. Traditional algorithms for cleaning point cloud corruption require the selection of appropriate parameters based on the characteri...
Point clouds provide a compact representation of 3D shapes however, the imperfections in acquisition processes corrupt point clouds by noise and give rise to a decrease in their power for representing 3D shapes. Learning-based denoising methods opera...
Point-cloud registration is a fundamental task in computer vision. However, most point clouds are partially overlapping, corrupted by noise and comprised of indistinguishable surfaces, especially for complexly distributed outdoor LiDAR point clouds,...
Due to the fact that point clouds are always corrupted by significant noise and large transformations, aligning two point clouds by deep neural networks is still challenging. This paper presents a semi-supervised point cloud registration (PCR) method...
LiDAR sensors have been shown to generate data with various common corruptions, which seriously affect their applications in 3D vision tasks, particularly object detection. At the same time, it has been demonstrated that traditional defense strategie...
As for rock numerical calculation and stability analysis, it is essential to build a numerical model of rock mass with concise and accurate structure information through the three-dimensional surface reconstruction of rock-mass point clouds. However,...
Rotation-invariant (RI) point cloud models aim to reduce sensitivity to viewpoint changes, but their performance still drops noticeably in real-world settings when local geometry is degraded by noise, occlusion, and uneven sampling. Once these distur...
Lane-level self-localization is essential for autonomous driving. Point cloud maps are typically used for self-localization but are known to be redundant. Deep features produced by neural networks can be used as a map, but their simple utilization co...
Airborne laser scanning (ALS) point cloud has been widely used in the fields of ground powerline surveying, forest monitoring, urban modeling, and so on because of the great convenience it brings to people’s daily life. However, the sparsity an...
As consumer-grade depth sensors provide an efficient and low-cost way to obtain point cloud data, an increasing number of applications regarding the acquisition and processing of livestock point clouds have been proposed. Curve skeletons are abstract...
Three-dimensional (3D) object detection constitutes a fundamental task in the field of environmental perception. While LiDAR provides high-precision 3D geometric data, its performance significantly degrades under adverse weather conditions like dense...
Light Detection and Ranging (LiDAR) is fundamental to autonomous driving and robotics, as it provides reliable 3D geometric information. However, snowfall introduces numerous spurious reflections that corrupt range measurements and severely degrade d...
This paper presents a fast Lidar inertial odometry and mapping (F-LIOM) method for mobile robot navigation on flat terrain with high real-time pose estimation, map building, and place recognition. Existing works on Lidar inertial odometry have mostly...