MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes

• URL: http://arxiv.org/abs/2302.12249v1
• Date: Thu, 23 Feb 2023 18:59:07 GMT
• Title: MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes
• Authors: Christian Reiser and Richard Szeliski and Dor Verbin and Pratul P. Srinivasan and Ben Mildenhall and Andreas Geiger and Jonathan T. Barron and Peter Hedman
• Abstract summary: We present a Memory-Efficient Radiance Field representation that achieves real-time rendering of large-scale scenes in a browser. We introduce a novel contraction function that maps scene coordinates into a bounded volume while still allowing for efficient ray-box intersection.
• Score: 61.01853377661283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural radiance fields enable state-of-the-art photorealistic view synthesis. However, existing radiance field representations are either too compute-intensive for real-time rendering or require too much memory to scale to large scenes. We present a Memory-Efficient Radiance Field (MERF) representation that achieves real-time rendering of large-scale scenes in a browser. MERF reduces the memory consumption of prior sparse volumetric radiance fields using a combination of a sparse feature grid and high-resolution 2D feature planes. To support large-scale unbounded scenes, we introduce a novel contraction function that maps scene coordinates into a bounded volume while still allowing for efficient ray-box intersection. We design a lossless procedure for baking the parameterization used during training into a model that achieves real-time rendering while still preserving the photorealistic view synthesis quality of a volumetric radiance field.

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