Spatial Cache Data Structures with Python Bind implementation. The C++ code in this repo is fully templated and I try to incorporate as much modern C++ programming (like perfect-forwarding, rvalue references, conditional compilation and SFINAE), as well as macro programming. This repo is expected to develop high performance CPU/GPU end spatial caching structures for point clouds and graphics primitives, etc.
Currently supported features:
- CPU end: Static-Multi-Tree (like Quad, Oct-Tree): dynamic insert (deletion not supported yet), fully templated (with visualization). C++/Python API
- CPU end: k-d Tree: tested, dynamic insert (deletion not supported yet). C++/Python API. Even faster than Quad-tree (5-10x).
| quad-tree space subdivision | k-d tree space space subdivision | Nearest neighbor search |
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