We introduce Multiresolution Deep Implicit Functions (MDIF), a hierarchical
representation that can recover fine geometry detail, while being able to
perform global operations such as shape completion. Our model represents a
complex 3D shape with a hierarchy of latent grids, which can be decoded into
different levels of detail and also achieve better accuracy. For shape
completion, we propose latent grid dropout to simulate partial data in the
latent space and therefore defer the completing functionality to the decoder
side. This along with our multires design significantly improves the shape
completion quality under decoder-only latent optimization. To the best of our
knowledge, MDIF is the first deep implicit function model that can at the same
time (1) represent different levels of detail and allow progressive decoding;
(2) support both encoder-decoder inference and decoder-only latent
optimization, and fulfill multiple applications; (3) perform detailed
decoder-only shape completion. Experiments demonstrate its superior performance
against prior art in various 3D reconstruction tasks.

本文提出了一种名为 MDIF 的多分辨率深度隐式函数，该模型可以在进行 3D 重建时恢复精细的几何细节，并支持多种应用，其创新点在于可以同时表示不同细节级别并支持逐步编码，支持编码器 - 解码器推理、仅解码器潜在空间优化，并在形状完成任务下获得优于现有技术的性能表现。