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Shape priors learned from data are commonly used to reconstruct 3D objectsfrom partial or noisy data. Yet no such shape priors are available for indoorscenes, since typical 3D autoencoders cannot handle their scale, complexity, ordiversity. In this paper, we introduce Local Implicit Grid Representations, anew 3D shape representation designed for scalability and generality. Themotivating idea is that most 3D surfaces share geometric details at some scale-- i.e., at a scale smaller than an entire object and larger than a smallpatch. We train an autoencoder to learn an embedding of local crops of 3Dshapes at that size. Then, we use the decoder as a component in a shapeoptimization that solves for a set of latent codes on a regular grid ofoverlapping crops such that an interpolation of the decoded local shapesmatches a partial or noisy observation. We demonstrate the value of thisproposed approach for 3D surface reconstruction from sparse point observations,showing significantly better results than alternative approaches.

Local Implicit Grid Representations for 3D Scenes