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.
@article{arxiv.2302.12249,
title = {MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes},
author = {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},
journal= {arXiv preprint arXiv:2302.12249},
year = {2023}
}
Comments
Video and interactive web demo available at https://merf42.github.io