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Efficiently reconstructing complex and intricate surfaces at scale is along-standing goal in machine perception. To address this problem we introduceDeep Local Shapes (DeepLS), a deep shape representation that enables encodingand reconstruction of high-quality 3D shapes without prohibitive memoryrequirements. DeepLS replaces the dense volumetric signed distance function(SDF) representation used in traditional surface reconstruction systems with aset of locally learned continuous SDFs defined by a neural network, inspired byrecent work such as DeepSDF. Unlike DeepSDF, which represents an object-levelSDF with a neural network and a single latent code, we store a grid ofindependent latent codes, each responsible for storing information aboutsurfaces in a small local neighborhood. This decomposition of scenes into localshapes simplifies the prior distribution that the network must learn, and alsoenables efficient inference. We demonstrate the effectiveness andgeneralization power of DeepLS by showing object shape encoding andreconstructions of full scenes, where DeepLS delivers high compression,accuracy, and local shape completion.

Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction