Abstract
Glossy objects present a significant challenge for 3D reconstruction from multi-view input images under natural lighting. In this paper, we introduce PBIR-NIE, an inverse rendering framework designed to holistically capture the geometry, material attributes, and surrounding illumination of such objects. We propose a novel parallax-aware non-distant environment map as a lightweight and efficient lighting representation, accurately modeling the near-field background of the scene, which is commonly encountered in real-world capture setups. This feature allows our framework to accommodate complex parallax effects beyond the capabilities of standard infinite-distance environment maps. Our method optimizes an underlying signed distance field (SDF) through physics-based differentiable rendering, seamlessly connecting surface gradients between a triangle mesh and the SDF via neural implicit evolution (NIE). To address the intricacies of highly glossy BRDFs in differentiable rendering, we integrate the antithetic sampling algorithm to mitigate variance in the Monte Carlo gradient estimator. Consequently, our framework exhibits robust capabilities in handling glossy object reconstruction, showcasing superior quality in geometry, relighting, and material estimation.
Guangyan Cai
Ph.D. Candidate in Computer Science
I am interested in physics-based differentiable rendering and its applications, such as inverse rendering.
Fujun Luan
Research Scientist at Adobe Research
Miloš Hašan
Senior Research Scientist at Adobe Research
Kai Zhang
Research Scientist at Adobe Research
Sai Bi
Research Scientist at Adobe Research
Zexiang Xu
Research Scientist at Adobe Research
Iliyan Georgiev
Senior Research Scientist at Adobe Research
