Bits-to-Photon: End-to-End Learned Scalable Point Cloud Compression for Direct Rendering
CoRR(2024)
摘要
Point cloud is a promising 3D representation for volumetric streaming in
emerging AR/VR applications. Despite recent advances in point cloud
compression, decoding and rendering high-quality images from lossy compressed
point clouds is still challenging in terms of quality and complexity, making it
a major roadblock to achieve real-time 6-Degree-of-Freedom video streaming. In
this paper, we address this problem by developing a point cloud compression
scheme that generates a bit stream that can be directly decoded to renderable
3D Gaussians. The encoder and decoder are jointly optimized to consider both
bit-rates and rendering quality. It significantly improves the rendering
quality while substantially reducing decoding and rendering time, compared to
existing point cloud compression methods. Furthermore, the proposed scheme
generates a scalable bit stream, allowing multiple levels of details at
different bit-rate ranges. Our method supports real-time color decoding and
rendering of high quality point clouds, thus paving the way for interactive 3D
streaming applications with free view points.
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