FSVFG: Towards Immersive Full-Scene Volumetric Video Streaming with Adaptive Feature Grid

Daheng Yin, Jianxin Shi, Miao Zhang, Zhaowu Huang, Jiangchuan Liu, Fang Dong

Abstract: Given the truly immersive viewing experiences, full-scene volumetric videos have received increasing attention from both academia and industry. Their vast data volumes, however, present significant challenges for real-time streaming over today's bandwidth-limited Internet. Considering the vast amount of full-scene volumetric data to be streamed and the limited bandwidth on the Internet, achieving adaptive full-scene volumetric video streaming over the Internet presents a significant challenge. Inspired by the advantages offered by neural fields, especially the feature grid method, we propose FSVFG, a novel full-scene volumetric video streaming system integrated feature grids as the representation of volumetric content. FSVFG employs an incremental training approach for feature grids and stores the features and residuals between adjacent grids as frames. To support adaptive streaming, we delve into the data structure and rendering processes of feature grids and propose bandwidth adaptation mechanisms. The mechanisms involve a coarse ray-marching for the selection of features and residuals to be sent, and achieve variable bitrate streaming by Level-of-Detail (LoD) and residual filtering. Based on these mechanisms, FSVFG achieves adaptive streaming by adaptively balancing the transmission of feature and residual according to the available bandwidth. Our preliminary results demonstrate the effectiveness of FSVFG, demonstrating its ability to improve visual quality and reduce bandwidth requirements of full-scene volumetric video streaming.