Scalable Human-Machine Point Cloud Compression

arXiv:2402.12532v1 Announce Type: new
Abstract: Due to the limited computational capabilities of edge devices, deep learning inference can be quite expensive. One remedy is to compress and transmit point cloud data over the network for server-side processing. Unfortunately, this approach can be sensitive to network factors, including available bitrate. Luckily, the bitrate requirements can be reduced without sacrificing inference accuracy by using a machine task-specialized codec. In this paper, we present a scalable codec for point-cloud data that is specialized …

abstract arxiv capabilities cloud cloud data compression computational cs.cv data deep learning deep learning inference devices edge edge devices eess.iv human inference machine network processing requirements scalable server type