Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object
classification, segmentation and detection is often laborious owing to the irregular structure …

Point clouds can be represented in many forms (views), typically, point-based sets, voxel-
based cells or range-based images (ie, panoramic view). The point-based view is …

LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously,
capture precise geometry of the surrounding environment and are crucial to many …

Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

Existing 3D human pose estimators suffer poor generalization performance to new datasets,
largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this …

Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent
upsampling approaches aim to generate a dense point set, while achieving both distribution …

To address the huge labeling cost in large-scale point cloud semantic segmentation, we
propose a novel hybrid contrastive regularization (HybridCR) framework in weakly …

This paper introduces data augmentation for point clouds by interpolation between
examples. Data augmentation by interpolation has shown to be a simple and effective …

As the basic task of point cloud analysis, classification is fundamental but always
challenging. To address some unsolved problems of existing methods, we propose a …