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Conjugate Gradient in Noisy Photometric Stereo
2014<p>This paper discusses the problem of reconstructing the Lambertian surface from noisy three-light source Photometric Stereo. In the continuous image setting the shape recovery process is divided into two steps: an algebraic one (gradient computation) and analytical one (gradient integration).
Ryszard Kozera +1 more
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Mutual illumination photometric stereo. [PDF]
, 2018 Many techniques have been developed in computer vision to recover three-dimensional shape from two-dimensional images. These techniques impose various combinations of assumptions/restrictions of conditions to produce a representation of shape (e.g. surface normals or a height map). Although great progress has been made it is a problem which remains far
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Testing Photometric Stereo Applications
2022 9th International Conference on Dependable Systems and Their Applications (DSA), 2022Ledio Jahaj, Franz Wotawa
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6th International Conference on Image Processing and its Applications, 1997
A novel variant of photometric stereo, termed reflective photometric stereo (RPS), has been presented. RPS exploits differences in intensity measurements captured under direct and reflected illumination to recover surface orientation from pairs of grey level images.
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A novel variant of photometric stereo, termed reflective photometric stereo (RPS), has been presented. RPS exploits differences in intensity measurements captured under direct and reflected illumination to recover surface orientation from pairs of grey level images.
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SRPS–deep-learning-based photometric stereo using superresolution images
Journal of Computational Design and Engineering, 2021Seok Chung, Minho Chang, Chung Seok
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Symmetric-light Photometric Stereo
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022Kazuma Minami +3 more
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SPS-Net: Self-Attention Photometric Stereo Network
IEEE Transactions on Instrumentation and Measurement, 2021Yunhui Yan, Han Yu, Huiyu Liu
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A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019Boxin Shi, Sai-Kit Yeung, Zhipeng Mo
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