Learning-Deep-Learning
Occupancy Networks: Learning 3D Reconstruction in Function Space
May 2023
tl;dr: Encoding the occupancy of a scene with a neural network, and can be queried at any location with arbitrary resolution.
Overall impression
In 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology.
Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. Instead of predicting a voxelized representation at a fixed resolution, the network can be evaluated at arbitrary resolution. This drastically reduces the memory footprint during training.
Key ideas
- Training involves random sampling points inside the volume. Random sampling yields the best results.
- During inference, a Multiresolution IsoSurface Extraction (MISE) method is used to extract isosurface of a scene.
Technical details
- Summary of technical details