Learning Photometric Feature Transform for Free-form Object Scan

• URL: http://arxiv.org/abs/2308.03492v1
• Date: Mon, 7 Aug 2023 11:34:27 GMT
• Title: Learning Photometric Feature Transform for Free-form Object Scan
• Authors: Xiang Feng, Kaizhang Kang, Fan Pei, Huakeng Ding, Jinjiang You, Ping Tan, Kun Zhou, Hongzhi Wu
• Abstract summary: We propose a novel framework to automatically learn to aggregate and transform photometric measurements from unstructured views. We build a system to reconstruct the geometry and anisotropic reflectance of a variety of challenging objects from hand-held scans. Results are validated against reconstructions from a professional 3D scanner and photographs, and compare favorably with state-of-the-art techniques.
• Score: 34.61673205691415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 fed to a multi-view stereo method 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 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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