Humans can naturally and effectively find salient regions in complex scenes. Motivated by
this observation, attention mechanisms were introduced into computer vision with the aim of …

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 …

Point cloud analysis is challenging due to irregularity and unordered data structure. To
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …

Vision-Language models like CLIP have been widely adopted for various tasks due to their
impressive zero-shot capabilities. However, CLIP is not suitable for extracting 3D geometric …

Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D
LiDAR data has led to rapid development in the field of autonomous driving. However …

Deep learning techniques for point cloud data have demonstrated great potentials in solving
classical problems in 3D computer vision such as 3D object classification and segmentation …

Multi-view projection methods have demonstrated their ability to reach state-of-the-art
performance on 3D shape recognition. Those methods learn different ways to aggregate …

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field
of computer vision and computer graphics. Regarding the task of 3D shape representation …

Abstract Domain Adaptation (DA) approaches achieved significant improvements in a wide
range of machine learning and computer vision tasks (ie, classification, detection, and …

In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in
point cloud completion: understanding faithful global shapes from incomplete point clouds …