  We study the problem of shape generation in 3D mesh representation from a
small number of color images with or without camera poses. While many previous
works learn to hallucinate the shape directly from priors, we adopt to further
improve the shape quality by leveraging cross-view information with a graph
convolution network. Instead of building a direct mapping function from images
to 3D shape, our model learns to predict series of deformations to improve a
coarse shape iteratively. Inspired by traditional multiple view geometry
methods, our network samples nearby area around the initial mesh's vertex
locations and reasons an optimal deformation using perceptual feature
statistics built from multiple input images. Extensive experiments show that
our model produces accurate 3D shapes that are not only visually plausible from
the input perspectives, but also well aligned to arbitrary viewpoints. With the
help of physically driven architecture, our model also exhibits generalization
capability across different semantic categories, and the number of input
images. Model analysis experiments show that our model is robust to the quality
of the initial mesh and the error of camera pose, and can be combined with a
differentiable renderer for test-time optimization.
