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Papers/HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned ...

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

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

2023-01-05CVPR 2023 1Novel View SynthesisVocal Bursts Intensity Prediction
PaperPDFCode(official)

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.

Results

TaskDatasetMetricValueModel
Novel View SynthesisDONeRF: Evaluation DatasetPSNR35.1HyperReel
Novel View SynthesisDONeRF: Evaluation DatasetPSNR33.1Instant NGP
Novel View SynthesisDONeRF: Evaluation DatasetPSNR30.9NeRF
Novel View SynthesisDONeRF: Evaluation DatasetPSNR30.9AdaNeRF
Novel View SynthesisDONeRF: Evaluation DatasetPSNR30.8DoNeRF
Novel View SynthesisDONeRF: Evaluation DatasetPSNR29.8TermiNeRF
Novel View SynthesisLLFFPSNR26.2HyperReel
Novel View SynthesisLLFFPSNR25.7AdaNeRF
Novel View SynthesisLLFFPSNR25.6Instant NGP
Novel View SynthesisLLFFPSNR23.6TermiNeRF
Novel View SynthesisLLFFPSNR22.9DoNeRF

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