3D shape reconstruction from a single image has been a long-standing problem in computer vision. The problem is ill-posed and highly challenging due to the information loss and occlusion that occurred during the imagery capture. In contrast to previous methods that learn holistic shape priors, we propose a method to learn spatial pattern priors for inferring the invisible regions of the underlying shape, wherein each 3D sample in the implicit shape representation is associated with a set of points generated by hand-crafted 3D mappings, along with their local image features. The proposed spatial pattern is significantly more informative and has distinctive descriptions on both visible and occluded locations. Most importantly, the key to our work is the ubiquitousness of the spatial patterns across shapes, which enables reasoning invisible parts of the underlying objects and thus greatly mitigates the occlusion issue. We devise a neural network that integrates spatial pattern representations and demonstrate the superiority of the proposed method on widely used metrics.
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