We study the problem of shape generation in 3D mesh representation from a few
color images with known camera poses. While many previous works learn to
hallucinate the shape directly from priors, we resort to further improving the
shape quality by leveraging cross-view information with a graph convolutional
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 shape 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, number of input images, and
quality of mesh initialization.

使用图卷积网络从多角度图像中预测一系列形状变形，以进一步提高 3D 网格表征的形状质量，从而获得高精确度、高可靠性且具有普适性的 3D 模型。