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 Deep Structured Implicit Functions (DSIF), a 3D shape representation that decomposes space into a structured set of local deep 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. Please see our video at https://youtu.be/HCBtG0-EZ2s

本文介绍了一种将三维表面图像表示成局部深度隐式函数并进行不同模型之间的泛化、空间分解等相关工作，并在实验中证明了该算法在表面重建和深度图像完成方面的效果优于现有算法。

局部深度隐式函数用于三维形状