  The goal of this project is to learn a 3D shape representation that enables
accurate 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 introduce
Local Deep Implicit Functions (LDIF), a 3D shape representation that decomposes
space into a structured set of learned implicit functions. We provide networks
that infer the space decomposition and local deep implicit functions from a 3D
mesh or posed depth image. During experiments, we find that it provides 10.3
points higher surface reconstruction accuracy (F-Score) than the
state-of-the-art (OccNet), while requiring fewer than 1 percent of the network
parameters. Experiments on posed depth image completion and generalization to
unseen 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 consistency
across diverse shape collections.
