Differentiable Point-based Inverse Rendering

• URL: http://arxiv.org/abs/2312.02480v2
• Date: Mon, 25 Mar 2024 06:22:09 GMT
• Title: Differentiable Point-based Inverse Rendering
• Authors: Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek,
• Abstract summary: DPIR is an analysis-by-synthesis method that processes images captured under diverse illuminations to estimate shape and spatially-varying BRDF. We devise a hybrid point-volumetric representation for geometry and a regularized basis-BRDF representation for reflectance. Our evaluations demonstrate that DPIR outperforms prior works in terms of reconstruction accuracy, computational efficiency, and memory footprint.
• Score: 9.88708409803907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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