Multiresolution Deep Implicit Functions for 3D Shape Representation
This addresses the problem of detailed and flexible 3D shape representation for computer vision and graphics applications, presenting a novel approach with specific improvements.
The paper tackles 3D shape representation by introducing Multiresolution Deep Implicit Functions (MDIF), a hierarchical model that recovers fine geometry detail and enables global operations like shape completion, achieving superior performance in 3D reconstruction tasks compared to prior methods.
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.