Multiresolution Deep Implicit Functions for 3D Shape Representation
Abstract
We introduce Multiresolution Deep Implicit Functions (MDIF), a hierarchical representation that can recover fine details, while being able to perform more 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 resolutions. Training is performed in an encoder-decoder manner, while the decoder-only optimization is supported during inference, hence can better generalize to novel objects, especially when performing shape completion. To the best of our knowledge, MDIF is the first model that can at the same time (1) reconstruct local detail; (2) perform decoder-only inference; (3) fulfill shape reconstruction and completion. We demonstrate superior performance against prior arts in our experiments.
Our model represents a complex 3D shape with a hierarchy of latent grids, which can be decoded into different resolutions. Training is performed in an encoder-decoder manner, while the decoder-only optimization is supported during inference, hence can better generalize to novel objects, especially when performing shape completion. To the best of our knowledge, MDIF is the first model that can at the same time (1) reconstruct local detail; (2) perform decoder-only inference; (3) fulfill shape reconstruction and completion. We demonstrate superior performance against prior arts in our experiments.
Research Areas
