FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic Data

• URL: http://arxiv.org/abs/2310.18279v2
• Date: Thu, 22 Aug 2024 15:20:20 GMT
• Title: FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic Data
• Authors: Oliver Boyne, Gwangbin Bae, James Charles, Roberto Cipolla,
• Abstract summary: We seek to develop a method for few-view reconstruction, for the case of the human foot. To solve this task, we must extract rich geometric cues from RGB images, before carefully fusing them into a final 3D object. We show that our normal predictor outperforms all off-the-shelf equivalents significantly on real images.
• Score: 27.53648027412686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface reconstruction from multi-view images is a challenging task, with solutions often requiring a large number of sampled images with high overlap. We seek to develop a method for few-view reconstruction, for the case of the human foot. To solve this task, we must extract rich geometric cues from RGB images, before carefully fusing them into a final 3D object. Our FOUND approach tackles this, with 4 main contributions: (i) SynFoot, a synthetic dataset of 50,000 photorealistic foot images, paired with ground truth surface normals and keypoints; (ii) an uncertainty-aware surface normal predictor trained on our synthetic dataset; (iii) an optimization scheme for fitting a generative foot model to a series of images; and (iv) a benchmark dataset of calibrated images and high resolution ground truth geometry. We show that our normal predictor outperforms all off-the-shelf equivalents significantly on real images, and our optimization scheme outperforms state-of-the-art photogrammetry pipelines, especially for a few-view setting. We release our synthetic dataset and baseline 3D scans to the research community.

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