LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming
This addresses the challenge of streaming 3D online worlds in bandwidth-constrained XR environments, offering an incremental improvement over existing 3DGS representations.
The paper tackled the problem of efficient 3D streaming for Extended Reality by proposing LapisGS, a layered 3D Gaussian Splatting method that adapts to bandwidth constraints, resulting in up to 50.71% improvement in SSIM and 286.53% improvement in LPIPS with 23% of the original model size.
The rise of Extended Reality (XR) requires efficient streaming of 3D online worlds, challenging current 3DGS representations to adapt to bandwidth-constrained environments. This paper proposes LapisGS, a layered 3DGS that supports adaptive streaming and progressive rendering. Our method constructs a layered structure for cumulative representation, incorporates dynamic opacity optimization to maintain visual fidelity, and utilizes occupancy maps to efficiently manage Gaussian splats. This proposed model offers a progressive representation supporting a continuous rendering quality adapted for bandwidth-aware streaming. Extensive experiments validate the effectiveness of our approach in balancing visual fidelity with the compactness of the model, with up to 50.71% improvement in SSIM, 286.53% improvement in LPIPS with 23% of the original model size, and shows its potential for bandwidth-adapted 3D streaming and rendering applications.