“MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes” by Szeliski, Reiser, Verbin, Srinivasan, Mildenhall, et al. …

  • ©Richard Szeliski, Christian Reiser, Dor Verbin, Pratul P. Srinivasan, Ben Mildenhall, Andreas Geiger, Jonathan T. Barron, and Peter Hedman




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

Session/Category Title: Cloud Rendering: Your GPU Is Somewhere Else




    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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