Deep 3D Representation

We introduce Multiresolution Deep Implicit Functions (MDIF), a hierarchical representation that can recover fine 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 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. 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.

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teaser image of Multiresolution Deep Implicit Functions for 3D Shape Representation

Multiresolution Deep Implicit Functions for 3D Shape Representation

2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
Keywords: deep implicit functions, neural representation

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