Volumetric Video Compression Through Neural-based Representation
Résumé
Volumetric video offers immersive exploration and interaction in 3D space, revolutionizing visual storytelling.
Recently, Neural Radiance Fields (NeRF) have emerged as a powerful neural-based technique for generating high-fidelity images from 3D scenes. Building upon NeRF advancements, recent works have explored NeRF-based compression for static 3D scenes, in particular point cloud geometry. In this paper, we propose an end-to-end pipeline for volumetric video compression using neural-based representation. We represent 3D dynamic content as a sequence of NeRFs, converting the explicit representation to neural representation. Building on the insight of significant similarity between successive NeRFs, we propose to benefit from this temporal coherence: we encode the differences between consecutive NeRFs, achieving substantial bitrate reduction without noticeable quality loss. Experimental results demonstrate the superiority of our method for dynamic point cloud compression over geometry-based PCC codecs and comparable performance with state-of-the-art PCC codecs for high-bitrate volumetric videos. Moreover, our proposed compression based on NeRF generalizes to arbitrary dynamic 3D content.
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