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arXiv · 2605.18054

CATRF: Codec-Adaptive TriPlane Radiance Fields for Volumetric Content Delivery

Tung-I Chen·Lingdong Wang·Subhransu Maji·Ramesh K. Sitaraman
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Abstract

Volumetric media promises next-generation content delivery applications, but its bandwidth demand remains a key bottleneck. Implicit and hybrid volumetric representations reduce model sizes, yet still require careful coding to reach 2D video-like bitrates. We present CATRF, a standard-codec-in-the-loop compression framework for plane-factorized radiance fields. During training, we quantize and pack 2D feature planes into codec-friendly canvases, run a standard codec roundtrip (JPEG/VP9/HEVC/AV1), then unpack and dequantize the decoded features before volume rendering. We use a straight-through estimator (STE) to insert the non-differentiable, standard codec pipeline into the training loop, allowing radiance-field features to adapt directly to the real, client-side codec distortions without introducing any learned codec parameters. On both static and dynamic benchmarks, CATRF consistently achieves a better rate-distortion trade-off over codec-agnostic and learned-codec-in-the-loop baselines, and also outperforms recent compressed 3DGS methods in both compression efficiency and decoding speed. These results highlight a practical path toward low-bitrate, compression-resilient volumetric representations for free-viewpoint video streaming.

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BibTeXRIS

Tung-I Chen, Lingdong Wang, Subhransu Maji, Ramesh K. Sitaraman. 2026-05-18. CATRF: Codec-Adaptive TriPlane Radiance Fields for Volumetric Content Delivery. https://arxiv.org/abs/2605.18054

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