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FRNN

A Fixed Radius Nearest Neighbors Search implemented on CUDA with similar interface as pytorch3d.ops.knn_points.

Performance

Performance

Algorithm Walkthrough & Experiment Results

FRNN Presentation

Depenency

Tested with cuda 10.2, python 3.8 and pytorch 1.6.0 on ubuntu 18.04.

Should be also fine other versions of cuda/python/pytorch.

Install

git clone --recursive https://github.com/lxxue/FRNN.git
# install a prefix_sum routine first
cd FRNN/external/prefix_sum
pip install .

# install FRNN
cd ../../ # back to the {FRNN} directory
# this might take a while since I instantiate all combinations of D and K
pip install -e .
# might need to restart the bash to make importing this package work

Usage

For fixed nearest neighbors search: doc

  # first time there is no cached grid
  dists, idxs, nn, grid = frnn.frnn_grid_points(
        points1, points2, lengths1, lengths2, K, r, grid=None, return_nn=False, return_sorted=True
  )
  # if points2 and r don't change, we can reuse the grid
  dists, idxs, nn, grid = frnn.frnn_grid_points(
        points1, points2, lengths1, lengths2, K, r, grid=grid, return_nn=False, return_sorted=True
  )

For manually gather nearest neighbors from idxs generated via frnn_grid_points: doc

  nn = frnn.frnn_gather(points2, idxs, lengths2)

Note

For small point clouds (e.g. < 10,000 points), the bruteforce way (e.g. pytorch3d's KNN) might be faster.

TODO

If you want a new feature, just open an issue or send me an email about it.

Acknowledgement

The code is build on the algorithm introduced by Rama C. Hoetzlein. I use the parallel prefix_sum routines implemented by mattdean1. I also learn (copy & paste) a lot from Pytorch3D's KNN implementations.

<!-- ## TODO 1. Fix the problem of error for long thin objects 2. Support dimensions for arbitrary D 3. Support K > 32 4. KNN grid implementations -->