This repository contains the source code for REGTR. REGTR utilizes multiple transformer attention layers to directly predict each downsampled point's corresponding location in the other point cloud. Unlike typical correspondence-based registration algorithms, the predicted correspondences are clean and do not require an additional RANSAC step. This results in a fast, yet accurate registration.
If you find this useful, please cite:
@inproceedings{yew2022regtr,
title={REGTR: End-to-end Point Cloud Correspondences with Transformers},
author={Yew, Zi Jian and Lee, Gim hee},
booktitle={CVPR},
year={2022},
}
Our model is trained with the following environment:
Other required packages can be installed using pip: pip install -r src/requirements.txt
.
Follow the following instructions to download each dataset (as necessary). Your folder should then look like this:
.
├── data/
├── indoor/
├── test/
| ├── 7-scenes-redkitchen/
| | ├── cloud_bin_0.info.txt
| | ├── cloud_bin_0.pth
| | ├── ...
| ├── ...
├── train/
| ├── 7-scenes-chess/
| | ├── cloud_bin_0.info.txt
| | ├── cloud_bin_0.pth
| | ├── ...
├── test_3DLoMatch_pairs-overlapmask.h5
├── test_3DMatch_pairs-overlapmask.h5
├── train_pairs-overlapmask.h5
└── val_pairs-overlapmask.h5
└── modelnet40_ply_hdf5_2048
├── ply_data_test0.h5
├── ply_data_test1.h5
├── ...
├── src/
└── Readme.md
Download the processed dataset from Predator project site, and place them into ../data
.
Then for efficiency, it is recommended to pre-compute the overlapping points (used for computing the overlap loss). You can do this by running the following from the src/
directory:
python data_processing/compute_overlap_3dmatch.py
Download the PointNet-processed dataset from here, and place it into ../data
.
You can download our trained models here. Unzip the files into the trained_models/
.
We provide a simple demo script demo.py
that loads our model and checkpoints, registers 2 point clouds, and visualizes the result. Simply download the pretrained models and run the following from the src/
directory:
python demo.py --example 0 # choose from 0 - 4 (see code for details)
Press 'q' to end the visualization and exit. Refer the documentation for visualize_result()
for explanation of the visualization.
The following code in the src/
directory performs evaluation using the pretrained checkpoints as provided above; change the checkpoint paths accordingly if you're using your own trained models. Note that due to non-determinism from the neighborhood computation during our GPU-based KPConv processing, the results will differ slightly (e.g. mean registration recall may differ by around +/- 0.2%) between each run.
This will run inference and compute the evaluation metrics used in Predator (registration success of <20cm).
# 3DMatch
python test.py --dev --resume ../trained_models/3dmatch/ckpt/model-best.pth --benchmark 3DMatch
# 3DLoMatch
python test.py --dev --resume ../trained_models/3dmatch/ckpt/model-best.pth --benchmark 3DLoMatch
# ModelNet
python test.py --dev --resume ../trained_models/modelnet/ckpt/model-best.pth --benchmark ModelNet
# ModelLoNet
python test.py --dev --resume ../trained_models/modelnet/ckpt/model-best.pth --benchmark ModelNet
Run the following commands from the src/
directory to train the network.
python train.py --config conf/3dmatch.yaml
python train.py --config conf/modelnet.yaml
We would like to thank the authors for Predator, D3Feat, KPConv, DETR for making their source codes public.