T3DNet: Compressing Point Cloud Models for Lightweight 3D Recognition

arXiv:2402.19264v1 Announce Type: new
Abstract: 3D point cloud has been widely used in many mobile application scenarios, including autonomous driving and 3D sensing on mobile devices. However, existing 3D point cloud models tend to be large and cumbersome, making them hard to deploy on edged devices due to their high memory requirements and non-real-time latency. There has been a lack of research on how to compress 3D point cloud models into lightweight models. In this paper, we propose a method …

abstract application arxiv autonomous autonomous driving cloud cs.cv deploy devices driving making memory mobile mobile devices recognition sensing them type