LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming

Yuang Shi, Géraldine Morin, Simone Gasparini, Wei Tsang Ooi

Keywords: Adaptive Streaming, 3D Gaussian Splatting, Level-of-Detail

TL;DR: We propose a layered progressive 3DGS, named LapisGS, that encodes multiple levels of detail into a single progressive model, facilitating adaptive and seamless streaming and rendering.

Abstract:

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.

Submission Number: 137