Bits-to-Photon: End-to-End Learned Scalable Point Cloud Compression for Direct Rendering

• URL: http://arxiv.org/abs/2406.05915v1
• Date: Sun, 9 Jun 2024 20:58:32 GMT
• Title: Bits-to-Photon: End-to-End Learned Scalable Point Cloud Compression for Direct Rendering
• Authors: Yueyu Hu, Ran Gong, Yao Wang,
• Abstract summary: We develop a point cloud compression scheme that generates a bit stream that can be directly decoded to renderable 3D Gaussians. 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.
• Score: 10.662358423042274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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