Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance.
The proposed DANI-Net differs from other PS or UPS methods in two aspects:
DANI-Net implements a differentiable shadow handling method that fully exploits shadow cues to solve UPS. DANI-Net can generate realistic soft shaodw maps given arbitrary light directions.
DANI-Net represents the spatially varying anisotropic specularity as a weighted sum of Anisotropic Spherical Gaussian (ASG). Thanks to ASG, DANI-Net can generate realistic appearance given arbitrary light conditions.
Given arbitrary directional lights, DANI-Net can relight the object with realistic appearance.
@inproceedings{li2023dani,
title={DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering},
author={Li, Zongrui and Zheng, Qian and Shi, Boxin and Pan, Gang and Jiang, Xudong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2023}}