English

Differentiable Point-based Inverse Rendering

Computer Vision and Pattern Recognition 2024-03-26 v2

Abstract

We present differentiable point-based inverse rendering, DPIR, an analysis-by-synthesis method that processes images captured under diverse illuminations to estimate shape and spatially-varying BRDF. To this end, we adopt point-based rendering, eliminating the need for multiple samplings per ray, typical of volumetric rendering, thus significantly enhancing the speed of inverse rendering. To realize this idea, we devise a hybrid point-volumetric representation for geometry and a regularized basis-BRDF representation for reflectance. The hybrid geometric representation enables fast rendering through point-based splatting while retaining the geometric details and stability inherent to SDF-based representations. The regularized basis-BRDF mitigates the ill-posedness of inverse rendering stemming from limited light-view angular samples. We also propose an efficient shadow detection method using point-based shadow map rendering. Our extensive evaluations demonstrate that DPIR outperforms prior works in terms of reconstruction accuracy, computational efficiency, and memory footprint. Furthermore, our explicit point-based representation and rendering enables intuitive geometry and reflectance editing.

Keywords

Cite

@article{arxiv.2312.02480,
  title  = {Differentiable Point-based Inverse Rendering},
  author = {Hoon-Gyu Chung and Seokjun Choi and Seung-Hwan Baek},
  journal= {arXiv preprint arXiv:2312.02480},
  year   = {2024}
}