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HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling

2023-01-05CVPR 2023Code Available2· sign in to hype

Benjamin Attal, Jia-Bin Huang, Christian Richardt, Michael Zollhoefer, Johannes Kopf, Matthew O'Toole, Changil Kim

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Abstract

Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques. However, the volume rendering procedures that drive these representations necessitate careful trade-offs in terms of quality, rendering speed, and memory efficiency. In particular, existing methods fail to simultaneously achieve real-time performance, small memory footprint, and high-quality rendering for challenging real-world scenes. To address these issues, we present HyperReel -- a novel 6-DoF video representation. The two core components of HyperReel are: (1) a ray-conditioned sample prediction network that enables high-fidelity, high frame rate rendering at high resolutions and (2) a compact and memory-efficient dynamic volume representation. Our 6-DoF video pipeline achieves the best performance compared to prior and contemporary approaches in terms of visual quality with small memory requirements, while also rendering at up to 18 frames-per-second at megapixel resolution without any custom CUDA code.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
DONeRF: Evaluation DatasetHyperReelPSNR35.1Unverified
DONeRF: Evaluation DatasetInstant NGPPSNR33.1Unverified
DONeRF: Evaluation DatasetNeRFPSNR30.9Unverified
DONeRF: Evaluation DatasetAdaNeRFPSNR30.9Unverified
DONeRF: Evaluation DatasetDoNeRFPSNR30.8Unverified
DONeRF: Evaluation DatasetTermiNeRFPSNR29.8Unverified
LLFFHyperReelPSNR26.2Unverified
LLFFAdaNeRFPSNR25.7Unverified
LLFFInstant NGPPSNR25.6Unverified
LLFFTermiNeRFPSNR23.6Unverified
LLFFDoNeRFPSNR22.9Unverified

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