  Deep learning based 3D shape generation methods generally utilize latent
features extracted from color images to encode the semantics of objects and
guide the shape generation process. These color image semantics only implicitly
encode 3D information, potentially limiting the accuracy of the generated
shapes. In this paper we propose a multi-view mesh generation method which
incorporates geometry information explicitly by using the features from
intermediate depth representations of multi-view stereo and regularizing the 3D
shapes against these depth images. First, our system predicts a coarse 3D
volume from the color images by probabilistically merging voxel occupancy grids
from the prediction of individual views. Then the depth images from multi-view
stereo along with the rendered depth images of the coarse shape are used as a
contrastive input whose features guide the refinement of the coarse shape
through a series of graph convolution networks. Notably, we achieve superior
results than state-of-the-art multi-view shape generation methods with 34%
decrease in Chamfer distance to ground truth and 14% increase in F1-score on
ShapeNet dataset.Our source code is available at https://git.io/Jmalg
