APCCPA '22
Wei Gao, Ge Li, Hui Yuan, Raouf Hamzaoui, Zhu Li, Shan Liu
Abstract
Point clouds are attracting much attention from academia, industry and standardization organizations such as MPEG, JPEG, and AVS. 3D Point clouds consisting of thousands or even millions of points with attributes can represent real-world objects and scenes in a way that enables an improved immersive visual experience and facilitates complex 3D vision tasks. In addition to various point cloud analysis and processing tasks (e.g., segmentation, classification, 3D object detection, registration), efficient compression for these large-scale 3D visual data is essential to make point cloud applications more effective. This workshop focuses on point cloud processing, analy sis, and compression in challenging situations to further improve visual experience and machine vision performance. Both learning-based and non-learning-based perception-oriented optimization algorithms for compression and processing are solicited. Contributions that advance the state-of-the-art in analysis tasks, are also welcomed.