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|a 10.1109/TVCG.2024.3515478
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|a eng
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|a Feng, Xiang
|e verfasserin
|4 aut
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|a Learning Photometric Feature Transform for Free-Form Object Scan
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|c 2025
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|a Text
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|a Date Revised 31.07.2025
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are subsequently fed to a multi-view stereo pipeline to enhance 3D reconstruction. The illumination conditions during acquisition and the feature transform are jointly trained on a large amount of synthetic data. We further build a system to reconstruct both the geometry and anisotropic reflectance of a variety of challenging objects from hand-held scans. The effectiveness of the system is demonstrated with a lightweight prototype, consisting of a camera and an array of LEDs, as well as an off-the-shelf tablet. Our results are validated against reconstructions from a professional 3D scanner and photographs, and compare favorably with state-of-the-art techniques
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|a Journal Article
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| 700 |
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|a Kang, Kaizhang
|e verfasserin
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| 700 |
1 |
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|a Pei, Fan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ding, Huakeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a You, Jinjiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tan, Ping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Kun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Hongzhi
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 31(2025), 9 vom: 22. Aug., Seite 6398-6409
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|g volume:31
|g year:2025
|g number:9
|g day:22
|g month:08
|g pages:6398-6409
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|u http://dx.doi.org/10.1109/TVCG.2024.3515478
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