English

BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis

Computer Vision and Pattern Recognition 2023-05-17 v2

Abstract

We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural volume-surface scene representation designed to have well-behaved level sets that correspond to surfaces in the scene. We then bake this representation into a high-quality triangle mesh, which we equip with a simple and fast view-dependent appearance model based on spherical Gaussians. Finally, we optimize this baked representation to best reproduce the captured viewpoints, resulting in a model that can leverage accelerated polygon rasterization pipelines for real-time view synthesis on commodity hardware. Our approach outperforms previous scene representations for real-time rendering in terms of accuracy, speed, and power consumption, and produces high quality meshes that enable applications such as appearance editing and physical simulation.

Keywords

Cite

@article{arxiv.2302.14859,
  title  = {BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis},
  author = {Lior Yariv and Peter Hedman and Christian Reiser and Dor Verbin and Pratul P. Srinivasan and Richard Szeliski and Jonathan T. Barron and Ben Mildenhall},
  journal= {arXiv preprint arXiv:2302.14859},
  year   = {2023}
}

Comments

Video and interactive web demo available at https://bakedsdf.github.io/

R2 v1 2026-06-28T08:52:17.548Z