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The goal of this project is to learn a 3D shape representation that enablesaccurate surface reconstruction, compact storage, efficient computation,consistency for similar shapes, generalization across diverse shape categories,and inference from depth camera observations. Towards this end, we introduceLocal Deep Implicit Functions (LDIF), a 3D shape representation that decomposesspace into a structured set of learned implicit functions. We provide networksthat infer the space decomposition and local deep implicit functions from a 3Dmesh or posed depth image. During experiments, we find that it provides 10.3points higher surface reconstruction accuracy (F-Score) than thestate-of-the-art (OccNet), while requiring fewer than 1 percent of the networkparameters. Experiments on posed depth image completion and generalization tounseen classes show 15.8 and 17.8 point improvements over the state-of-the-art,while producing a structured 3D representation for each input with consistencyacross diverse shape collections.

Local Deep Implicit Functions for 3D Shape