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.

使用少量视角重构人脚模型的方法，通过提取图像中的丰富几何线索并将它们融合到最终的 3D 对象中，证明了正常预测器在真实图像上的优越性能以及优于当今主流技术的优化方案，尤其适用于少量视角的情况。