FOUND: Foot Optimization with Uncertain Normals for Surface Deformation
  Using Synthetic Data
Oliver Boyne Gwangbin Bae James Charles Roberto Cipolla
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.