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https://github.com/louis030195/octree
Dynamic sparse bounded octree in Go
https://github.com/louis030195/octree
3d data-structures octree
Last synced: about 2 months ago
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Dynamic sparse bounded octree in Go
- Host: GitHub
- URL: https://github.com/louis030195/octree
- Owner: louis030195
- License: mit
- Created: 2020-03-22T16:46:36.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-12-07T20:44:55.000Z (about 4 years ago)
- Last Synced: 2024-06-20T10:06:16.755Z (6 months ago)
- Topics: 3d, data-structures, octree
- Language: Go
- Homepage:
- Size: 621 KB
- Stars: 9
- Watchers: 5
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# octree
[](https://app.codacy.com/gh/louis030195/octree?utm_source=github.com&utm_medium=referral&utm_content=louis030195/octree&utm_campaign=Badge_Grade_Dashboard)
[](https://pkg.go.dev/github.com/louis030195/octree)
Imagine, someday, you lose your wallet.
You remember having it until you came back home by driving.
You will therefore search for your wallet around your car, you won't search the whole universe to find your wallet,
logic no ?
That's the principle of an Octree data structure.
## Installation
```bash
go get -u github.com/louis030195/octree
```
## Usage
```go
package main
import (
octree "github.com/louis030195/octree/pkg"
"github.com/louis030195/protometry/api/volume"
"log"
)
func main() {
o := octree.NewOctree(volume.NewBoxMinMax(1, 1, 1, 4, 4, 4))
myObj := octree.NewObjectCube(0, 2, 2, 3, 0.5)
ok := o.Insert(*myObj)
log.Printf("%v", ok) // true
colliders := o.GetColliding(*volume.NewBoxMinMax(0, 0, 0, 0.9, 2.9, 0.9))
log.Printf("%v", colliders) // some objects
log.Printf("%v", o.Move(myObj, 2, 2, 3)) // Success
log.Printf("%v", o.Move(myObj, 2, 2, 8)) // Failure: outside tree !
}
```
## Benchmark
```bash
make bench
```
## Roadmap
- [ ] More test coverage
- [ ] Better benchmarks
- [ ] Possible optimisations
- Linear implementation
- Looseness
- Less copies, unnecessary operations
- Parallelization of a few steps
## References
- [Github storpipfugl/pykdtree: Fast kd-tree implementation in Python](https://github.com/storpipfugl/pykdtree)
- [Github Rust rust-octree](https://github.com/ybyygu/rust-octree)
- [Github JS sparse-octree](https://github.com/vanruesc/sparse-octree)
- [Github Distributed adaptive octree construction, 2:1 balancing & partitioning based on space filling curves](https://github.com/paralab/Dendro-5.01)
- [Github UnityOctree](https://github.com/Nition/UnityOctree)
- [Book: Real-Time Collision Detection - Christer Ericson](https://www.amazon.com/exec/obidos/tg/detail/-/1558607323?tag=realtimecolli-20)
- [AN OVERVIEW OF QUADTREES, OCTREES, AND RELATED HIERARCHICAL DATA STRUCTURES](https://www.cs.umd.edu/~hjs/pubs/Samettfcgc88-ocr.pdf)
- [Efficient Sparse Voxel Octrees](https://research.nvidia.com/publication/efficient-sparse-voxel-octrees)
- [An Efficient Parametric Algorithm for Octree Traversal](http://wscg.zcu.cz/wscg2000/Papers_2000/X31.pdf)
- [O-CNN: Octree-based Convolutional Neural Networks for 3D ShapeAnalysis](https://wang-ps.github.io/O-CNN_files/CNN3D.pdf)
- Behley, J.; Steinhage, V.; Cremers, A. B. Efficient Radius Neighbor Search in
Three-Dimensional Point Clouds. In 2015 IEEE International Conference on
Robotics and Automation (ICRA); 2015; pp 3625–3630.
- [scipy.spatial.cKDTree — SciPy Reference Guide](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.cKDTree.html)
- Milinda Fernando, David Neilsen, Hyun Lim, Eric Hirschmann, Hari Sundar, ”Massively Parallel Simulations of Binary Black Hole Intermediate-Mass-Ratio Inspirals” SIAM Journal on Scientific Computing 2019. [Paper](https://doi.org/10.1137/18M1196972)
- Milinda Fernando, David Neilsen, Hari Sundar, ”A scalable framework for Adaptive Computational General Relativity on Heterogeneous Clusters”, (ACM International Conference on Supercomputing, ICS’19)
- Milinda Fernando, Dmitry Duplyakin, and Hari Sundar. 2017. ”Machine and Application Aware Partitioning for Adaptive Mesh Refinement Applications”. In Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing (HPDC ’17). ACM, New York, NY, USA, 231-242. [Paper](https://doi.org/10.1145/3078597.3078610)