OctFormer
Segments large 3D point clouds using an octree to keep attention tractable.
- Field
- Cross-domain
- Method
- Deep learning
- Data it takes
- point-cloud
- License
- MIT
Transformer attention scales poorly with point count. OctFormer partitions the cloud into an octree so attention operates within local regions, which keeps memory manageable on scans too large for a dense transformer.
Relevant when a scan is big enough that other architectures run out of GPU memory before they run out of accuracy.
Catalog entry last checked 2026-08-19. All models →
Run OctFormer on your data
The lab runs this on its own compute, in a container, with the inputs and parameters recorded alongside the result. Initial scoping conversations are free.
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