Adaptive 3D Gaussian Splatting Video Streaming
This work addresses streaming challenges for 3DGS volumetric video, which is important for applications requiring high-quality 3D video transmission, though it appears incremental as it builds on existing 3DGS methods.
The paper tackles the challenge of streaming 3D Gaussian splatting (3DGS) videos, which have large data volumes and complex compression needs, by introducing a framework that uses Gaussian deformation fields, hybrid saliency tiling, and differentiated quality modeling to achieve efficient compression and adaptation to bandwidth fluctuations while ensuring high transmission quality.
The advent of 3D Gaussian splatting (3DGS) has significantly enhanced the quality of volumetric video representation. Meanwhile, in contrast to conventional volumetric video, 3DGS video poses significant challenges for streaming due to its substantially larger data volume and the heightened complexity involved in compression and transmission. To address these issues, we introduce an innovative framework for 3DGS volumetric video streaming. Specifically, we design a 3DGS video construction method based on the Gaussian deformation field. By employing hybrid saliency tiling and differentiated quality modeling of 3DGS video, we achieve efficient data compression and adaptation to bandwidth fluctuations while ensuring high transmission quality. Then we build a complete 3DGS video streaming system and validate the transmission performance. Through experimental evaluation, our method demonstrated superiority over existing approaches in various aspects, including video quality, compression effectiveness, and transmission rate.