PBIR-NIE: Glossy Object Capture under Non-Distant Lighting
This addresses the problem of accurately reconstructing glossy objects for applications in computer vision and graphics, representing an incremental improvement with novel components for specific bottlenecks.
The paper tackles the challenge of 3D reconstruction for glossy objects under natural lighting by introducing PBIR-NIE, an inverse rendering framework that captures geometry, material attributes, and illumination, resulting in superior quality in geometry, relighting, and material estimation.
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.