Variational Uncalibrated Photometric Stereo Under General Lighting
Zhenzhang Ye, Bjoern Haefner, Maolin Gao, Tao Wu, Yvain Quéau, Daniel Cremers
摘要
Photometric stereo (PS) techniques nowadays remain constrained to an ideal laboratory setup where modeling and calibration of lighting is amenable. To eliminate such restrictions, we propose an efficient principled variational approach to uncalibrated PS under general illumination. To this end, the Lambertian reflectance model is approximated through a spherical harmonic expansion, which preserves the spatial invariance of the lighting. The joint recovery of shape, reflectance and illumination is then formulated as a single variational problem. There the shape estimation is carried out directly in terms of the underlying perspective depth map, thus implicitly ensuring integrability and bypassing the need for a subsequent normal integration. To tackle the resulting nonconvex problem numerically, we undertake a two-phase procedure to initialize a balloon-like perspective depth map, followed by a “lagged” block coordinate descent scheme. The experiments validate efficiency and robustness of this approach. Across a variety of evaluations, we are able to reduce the mean angular error consistently by a factor of 2–3 compared to the state-of-the-art.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
相关 Paper
- Neural Multi-View Self-Calibrated Photometric Stereo without Photometric Stereo CuesXu Cao, Takafumi TaketomiICCV 2025 · 被引用 1 次
- Sparse Views, Near Light: A Practical Paradigm for Uncalibrated Point-Light Photometric StereoMohammed Brahimi, Bjoern Haefner, Zhenzhang Ye, Bastian Goldluecke 等CVPR 2024
- Universal Photometric Stereo Network using Global Lighting ContextsSatoshi IkehataCVPR 2022 · 被引用 22 次
- Stereoscopic Flash and No-Flash Photography for Shape and Albedo RecoveryXu Cao, Michael Waechter, Boxin Shi, Ye Gao 等CVPR 2020
- Uncalibrated Neural Inverse Rendering for Photometric Stereo of General SurfacesBerk Kaya, Suryansh Kumar, Carlos E. P. de Oliveira, Vittorio Ferrari 等CVPR 2021
