MPS-Net: Learning to recover surface normal for multispectral photometric stereo
| Autor: | Feng Gao, Yakun Ju, Xinghui Dong, Junyu Dong, Lin Qi, Jichao He |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Surface (mathematics)
0209 industrial biotechnology business.industry Computer science Cognitive Neuroscience Multispectral image MathematicsofComputing_NUMERICALANALYSIS ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Reflectivity Computer Science Applications Image (mathematics) 020901 industrial engineering & automation Photometric stereo Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Net (polyhedron) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Normal ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Neurocomputing. 375:62-70 |
| ISSN: | 0925-2312 |
| Popis: | Multispectral Photometric Stereo (MPS) estimates per-pixel surface normals from one single image captured under three colored (red, green and blue) light sources. Unlike traditional Photometric Stereo, MPS can therefore be used in dynamic scenes for single frame reconstruction. However, MPS is challenging due to the tangle of the illumination, surface reflectance and camera response, causing inaccurate estimation of surface normal. Existing approaches rely on either extra depth information or materials calibration strategies, thus limiting its usage in practical applications. In this paper, we propose a Multispectral Photometric Stereo Network (MPS-Net) to solve this under-determined system. The MPS-Net takes the single multispectral image and an initial surface normal estimation obtained from this image itself, and outputs an accurate surface normal map, where no extra depth or materials calibration information is required. We show that the MPS-Net is not constrained to Lambertian surfaces and can be applied to surfaces with complex reflectance. We evaluated the MPS-Net using both synthetic and real objects of various materials. Our experiment results show that the MPS-Net outperforms the state-of-the-art approaches. |
| Databáze: | OpenAIRE |
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