2017 CVPR CVPR 2017

Semi-Calibrated Near Field Photometric Stereo

Abstract

3D reconstruction from shading information through Photometric Stereo is considered a very challenging problem in Computer Vision. Although this technique can potentially provide highly detailed shape recovery, its accuracy is critically dependent on a numerous set of factors among them the reliability of the light sources in emitting a constant amount of light. In this work, we propose a novel variational approach to solve the so called semi-calibrated near field Photometric Stereo problem, where the positions but not the brightness of the light sources are known. Additionally, we take into account realistic modeling features such as perspective viewing geometry and heterogeneous scene composition, containing both diffuse and specular objects. Furthermore, we also relax the point light source assumption that usually constraints the near field formulation by explicitly calculating the light attenuation maps. Synthetic experiments are performed for quantitative evaluation for a wide range of cases whilst real experiments provide comparisons, qualitatively outperforming the state of the art.

🌉 Interdisciplinary Bridge — Computer Vision and Machine Learning
🧭 Keyword Pioneer — near field lighting
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio