Deep Near-Light Photometric Stereo for Spatially Varying Reflectances
| Autor: | Hiroaki Santo, Yasuyuki Matsushita, Michael Waechter |
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| Rok vydání: | 2020 |
| Předmět: |
Image formation
Pixel business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Hybrid approach Lambertian reflectance Reconstruction error Photometric stereo Astrophysics::Solar and Stellar Astrophysics Computer vision Artificial intelligence business Normal ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Computer Vision – ECCV 2020 ISBN: 9783030585976 ECCV (8) |
| Popis: | This paper presents a near-light photometric stereo method for spatially varying reflectances. Recent studies in photometric stereo proposed learning-based approaches to handle diverse real-world reflectances and achieve high accuracy compared to conventional methods. However, they assume distant (i.e., parallel) lights, which can in practical settings only be approximately realized, and they fail in near-light conditions. Near-light photometric stereo methods address near-light conditions but previous works are limited to over-simplified reflectances, such as Lambertian reflectance. The proposed method takes a hybrid approach of distant- and near-light models, where the surface normal of a small area (corresponding to a pixel) is computed locally with a distant light assumption, and the reconstruction error is assessed based on a near-light image formation model. This paper is the first work to solve unknown, spatially varying, diverse reflectances in near-light photometric stereo. |
| Databáze: | OpenAIRE |
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