We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists
of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k …

Given the rapid development of 3D scanners, point clouds are becoming popular in AI-
driven machines. However, point cloud data is inherently sparse and irregular, causing …

Learning per-point semantic features from the hierarchical feature pyramid is essential for
point cloud semantic segmentation. However, most previous methods suffered from …

Point cloud completion aims to recover the complete shape based on a partial observation.
Existing methods require either complete point clouds or multiple partial observations of the …

Point cloud registration is an important task for other point cloud tasks. Feature-based
methods are widely adopted for their speed and efficiency in point cloud registration. The …

Object detection in three-dimensional (3D) space attracts much interest from academia and
industry since it is an essential task in AI-driven applications such as robotics, autonomous …

Abstract has gradually become the first choice of modern urban public transportation due to
its advantages of safety and high-efficiency. Shield tunnel is an important type of subway …

Multi-view deep neural networks have shown excellent performance on 3D shape
classification tasks. However, global features aggregated from multiple views data often lack …

LiDAR has become a vital sensor for autonomous driving scene understanding. To meet the
accuracy and speed of LiDAR point clouds semantic segmentation, an efficient model …

Semantic segmentation of 3D scenes is one of the most important tasks in the field of
computer vision and has attracted much attention. In this paper, we propose a novel …