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i-Octree: A Fast, Lightweight, and Dynamic Octree for Proximity Search(Accepted by ICRA 2024)

i-Octree is a dynamic octree data structure that supports both fast nearest neighbor search and real-time dynamic updates, such as point insertion, deletion, and on-tree down-sampling. The i-Octree is built upon a leaf-based octree and has two key features: a local spatially continuous storing strategy that allows for fast access to points while minimizing memory usage, and local on-tree updates that significantly reduce computation time compared to existing static or dynamic tree structures.

Features

  • Dynamically insert points to the tree.
  • Delete points inside given axis-aligned bounding boxes.
  • Fast k-nearest neighbors search.
  • Fast radius neighbors search.
  • Fully templated for maximal flexibility to support arbitrary point representations & containers.

News 📰

[2024.03.16] - Feature Enhancement

  • Enhanced the implementation of the i-Octree with new functionalities and updated the Python bindings accordingly.

Python Bindings Test

1. Requirement

To compile, we require Eigen, C++17, and torch.

2. Run

git clone [email protected]:zhujun3753/i-octee.git

cd octree_map
# Update the `CMAKE_PREFIX_PATH` variable in `CMakeLists.txt` to reflect your own path settings.!!!!
bash build.sh

cd ..
python demo.py

3. Results

==============================
This is a debug print in OctreeMap C++!
==============================
num: 100
attr_n: 6
after filter num: 95
octree_feature.get_size(): 85
tensor([0.5879, 0.8644, 0.9247, 0.9912, 0.9457, 0.2752, 0.5103, 0.7180, 0.9304])
tensor([0.5879, 0.8644, 0.9247, 0.9912, 0.9457, 0.2752, 0.5103, 0.7180, 0.9304])

Run Randomized Data Experiments

1. Build

git clone [email protected]:zhujun3753/i-octee.git

# For Comparison
cd i-octree
git clone [email protected]:hku-mars/ikd-Tree.git

# Build & Run
bash run.sh

# Plot Results
python plot_time.py

2. Results

Attribution

If you use the implementation or ideas from the corresponding paper in your academic work, it would be nice if you cite the corresponding paper:

@misc{zhu2023ioctree,
      title={i-Octree: A Fast, Lightweight, and Dynamic Octree for Proximity Search}, 
      author={Jun Zhu and Hongyi Li and Shengjie Wang and Zhepeng Wang and Tao Zhang},
      year={2023},
      eprint={2309.08315},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

Acknowledgement

Thanks to Jens Behley for open-sourcing his excellent work octree.

This project uses "ikd-Tree" by Cai, Yixi for comparison purposes. The code from "ikd-Tree" is licensed under the GPL-2.0. You can find the original project and its source code here.

License

The source code of i-Octree is released under GPLv2 license. For commercial use, please contact Mr. Jun ZHU ([email protected]) or Dr. Tao ZHANG ([email protected]).