PREDATOR: Registration of 3D Point Clouds with Low Overlap
An inofficial PyTorch implementation of PREDATOR based on KPConv.
The code has been tested on Ubuntu 16.4, Python 3.7, PyTorch (1.7.1+cu101), torchvision (0.8.2+cu101), GCC 5.4.0 and Open3D (0.9 or 0.13).
All experiments were run on a Tesla V100 GPU with an Intel 6133CPU at 2.50GHz CPU.
Download 3DMatch
We adopted the 3DMatch provided from PREDATOR, and download it from here [5.17G]. Unzip it, then we should get the following directories structure:
| -- indoor
| -- train (#82, cats: #54)
| -- 7-scenes-chess
| -- 7-scenes-fire
| -- ...
| -- sun3d-mit_w20_athena-sc_athena_oct_29_2012_scan1_erika_4
| -- test (#8, cats: #8)
| -- 7-scenes-redkitchen
| -- sun3d-home_md-home_md_scan9_2012_sep_30
| -- ...
| -- sun3d-mit_lab_hj-lab_hj_tea_nov_2_2012_scan1_erika
Compile python bindings and Reconfigure
# Compile
cd PREDATOR/cpp_wrappers
sh compile_wrappers.sh
cd ..
# Reconfigure configs/threedmatch.yaml by updating the following values based on your dataset.
exp_dir: your_saved_path for checkpoints and summary.
checkpoint: your_ckpt_path; it's just required during evaluating and visualizing.
root: your_data_path for the indoor.
Train
cd PREDATOR
python train.py
(Optional) Download pretrained weights
Download pretrained weights [baidu disk, 28.36M] with password 0zfl for the following evaluation and visualization.
Evaluate
cd PREDATOR
python evaluate.py
Visualize
cd PREDATOR
python vis.py
Results on 3DMatch
| npoints | Inlier Ratio | Feature Match Recall | Registration Recall | Weighted Registration Recall |
|---|---|---|---|---|
| 5000 | 0.519 | 0.964 | 0.903 | 0.929 |
| 1000 | 0.518 | 0.962 | 0.898 | 0.918 |
Note: We calculate Registration Recall and Weighted Registration Recall based on equation (3) in PREDATOR Supplementary. It’s a little different from implementation in OverlapPredator, which is reported in the paper.
Acknowledgements
Thanks for the open source code OverlapPredator, and KPConv.pytorch.
