An inofficial PyTorch implementation of PREDATOR based on KPConv.

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

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, KPConv-PyTorch and KPConv.pytorch.