Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
| Autor: | Berk Kaya, Suryansh Kumar, Vittorio Ferrari, Luc Van Gool, Carlos Manta Oliveira |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Surface (mathematics)
Image formation FOS: Computer and information sciences Artificial neural network business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Process (computing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Iterative reconstruction Photometric stereo Computer Science::Computer Vision and Pattern Recognition Computer vision Artificial intelligence Bidirectional reflectance distribution function business Surface reconstruction ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | CVPR 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Popis: | This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it is challenging to procure both of this information precisely, which restricts the broader adoption of photometric stereo algorithms for vision application. To bypass this difficulty, we propose an uncalibrated neural inverse rendering approach to this problem. Our method first estimates the light directions from the input images and then optimizes an image reconstruction loss to calculate the surface normals, bidirectional reflectance distribution function value, and depth. Additionally, our formulation explicitly models the concave and convex parts of a complex surface to consider the effects of interreflections in the image formation process. Extensive evaluation of the proposed method on the challenging subjects generally shows comparable or better results than the supervised and classical approaches. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ISBN:978-1-6654-4509-2 ISBN:978-1-6654-4510-8 |
| Databáze: | OpenAIRE |
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