3DMatch Registration Dataset

With the rapid development on the topic of point cloud registration, current methods typically create their own custom registration dataset based on the indoor benchmark 3DMatch.

So this repo shows how to create your own point cloud registration dataset from the official 3DMatch benchmark.

Pipeline Overview

Note: You can generate these data yourself by following these steps, or you can just download the data that has already been generated -> download

3DMatch Dataset Explanation

3DMatch Reconstruction Datasets contain *62 .zip files, with each file corresponding to an indoor scene of RGB-D image sequences.

├── /data/3DMatch_zip/  
│   ├──	sun3d-brown_bm_1-brown_bm_1.zip
│   ├── sun3d-brown_bm_4-brown_bm_4.zip
│   ├── sun3d-brown_cogsci_1-brown_cogsci_1.zip
│   ├── ...

Then unzip all *.zip files, and one will get the output depth image sequences which are arranged like this:

├── /data/3DMatch_depth/
│   ├── sun3d-brown_bm_1-brown_bm_1/
│   │   ├── camera-intrinsics.txt  
│   │   ├── seq-01/
│   ├── sun3d-brown_bm_4-brown_bm_4/
│   │   ├── camera-intrinsics.txt  
│   │   ├── seq-01/
│   ├── sun3d-brown_cogsci_1-brown_cogsci_1
│   ├── ...

seq-0* contains a sequence of depth images of an indoor scene, which could be further used to create point clouds.

Train & Val & Test Split

As shown above, 3DMatch contains a total of 62 scenes of image sequences, which are officially split (split.txt) into 54 scenes for training, and 8 scenes for testing.

For 54 training scenes, one can further pick several scenes from them and categorize them as the val set. Here for example:

TSDF Fusion (depth images -> point clouds)

We use the original 3DMatch-toolbox for TSDF fusion, which is implemented with OpenCV and Matlab, although there is a much easier-to-use python alternative fuse_fragments_3DMatch.py for TSDF fusion, here we keep with the inconvenient but original way of creating point clouds.

• OS: ubuntu18.04
• CUDA: 11.1
• CUDNN: 8.2.1
• OpenCV 3.4.1
• Matlab R2017a

OpenCV Configuration

install OpenCV 3.4.1 (skip this if you can install OpenCV by yourself)

>> sudo apt install cmake
>> sudo apt install libgtk2.0-dev
>> sudo apt install pkg-config
>> sudo apt install ffmpeg
>> sudo apt install libavcodec-dev
>> sudo apt install libavformat-dev
>> sudo apt install libswscale-dev
>> unzip opencv-3.4.1.zip
>> cd opencv-3.4.1
>> mkdir build
>> cmake -D WITH_TBB=ON -D WITH_EIGEN=ON -D OPENCV_GENERATE_PKGCONFIG=ON  -D BUILD_DOCS=ON -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF  -D WITH_OPENCL=OFF -D WITH_CUDA=OFF -D BUILD_opencv_gpu=OFF -D BUILD_opencv_gpuarithm=OFF -D BUILD_opencv_gpubgsegm=O -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
>> make -j8
>> sudo make install
# write /usr/local/lib -> /etc/ld.so.conf.d/opencv.conf
# write export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig -> ~/.bashrc
>> source ~/.bashrc
>> ldconfig -v | grep opencv

Matlab Configuration

Somehow install Matlab on your system :)

I successfully install Matlab R2017a on ubuntu18.04 following this link.

Fuse Depth Images into Point Clouds (train & val set)

>> git clone https://github.com/andyzeng/3dmatch-toolbox.git
>> cd 3dmatch-toolbox/depth-fusion

modify compile.sh (mostly change the cuda and cudnn directory):

elif uname | grep -q Linux; then
  CUDA_LIB_DIR=/usr/local/cuda-11.1/lib64
  CUDNN_LIB_DIR=/usr/local/cuda-11.1/lib64
fi

CUDNN_INC_DIR=/usr/local/cuda-11.1/include

nvcc -std=c++11 -O3 -o demo demo.cu -I/usr/local/cuda-11.1/include -I$CUDNN_INC_DIR -L$CUDA_LIB_DIR -L$CUDNN_LIB_DIR -lcudart -lcublas -lcudnn -lcurand -D_MWAITXINTRIN_H_INCLUDED `pkg-config --cflags --libs opencv

>> bash compile.sh

It will generate demo file under the current folder.

Then modify the fuseSceneFragments.m (the fragmentsPath points to the folder that will contain the TSDF fusion results):

cudaBinPath = '/usr/local/cuda-11.1/bin';
cudaLibPath = '/usr/local/cuda-11.1/lib64';
cudnnLibPath = '/usr/local/cuda-11.1/lib64';
dataPath = '/data/3DMatch_depth'; 

fragmentsPath = '/data/3DMatch_output/train';
sceneList = {'sun3d-brown_bm_1-brown_bm_1', ...
             'sun3d-brown_cogsci_1-brown_cogsci_1', ...
             'sun3d-brown_cs_2-brown_cs2', ...
             xxxxx  % total 48 train scenes
             };
             
fragmentsPath = '/data/3DMatch_output/val';
sceneList = {'sun3d-brown_bm_4-brown_bm_4', ...
             'sun3d-harvard_c11-hv_c11_2', ...
             '7-scenes-heads', ...
             xxxxx  % total 6 val scenes
             };           

Then run fuseSceneFragments.m to get point clouds output:

## start TSDF fusion!
>> matlab -nodesktop -r fuseSceneFragments

After TSDF fusion, the /data/3DMatch_output/train will contain the fusion results of the 48 train scenes, while /data/3DMatch_output/val will contain the fusion results for 6 val scenes.

Now, the raw *.ply files of the train & val (48 + 6 scenes) sets have all been correctly generated, for example:

├── /data/3DMatch_output/
│   ├── train/
│   │   ├── sun3d-brown_bm_1-brown_bm_1/
│   │   │	├── cloud_bin_0.ply
│   │   │	├── cloud_bin_0.info.txt
│   │   │	├── ...
│   │   ├── sun3d-brown_cogsci_1-brown_cogsci_1/
│   │   │	├── cloud_bin_0.ply
│   │   │	├── cloud_bin_0.info.txt
│   │   │	├── ...
│   ├── val/
│   │   ├── sun3d-brown_bm_4-brown_bm_4/
│   │   │	├── cloud_bin_0.ply
│   │   │	├── cloud_bin_0.info.txt
│   │   │	├── ...
│   │   ├── sun3d-harvard_c11-hv_c11_2/
│   │   │	├── cloud_bin_0.ply
│   │   │	├── cloud_bin_0.info.txt
│   │   │	├── ...

the cloud_bin_x.info.txt contains the transformation matrix that will transform cloud_bin_x.ply to the global reference frame.

Download the Test set

As for the test set (8 scenes), there is no need to generate them yourself, certainly one can modify the sceneList and fragmentsPath in fuseSceneFragments.m to generate them, but the original 3DMatch website has provided them for us, along with ground-truth transformation which could be downloaded at Geometric Registration Benchmark on the website.

Download them and place them at /data/3DMatch_output/test.

Generate Overlap Info

In the task of Point Cloud Registration, one will need to compute the overlap information between fragments in order to generate training pairs:

>> python cal_overlap.py --root /data/3DMatch_output/train --output_json train_info.json --ol_thres 0.3
>> python cal_overlap.py --root /data/3DMatch_output/val --output_json val_info.json --ol_thres 0.3

the ol_thres indicates that we only generate info for pairs with overlap ratio > ol_thres.

then the overlap information for the train and the val set will be stored at train_info.json and val_info.json.

Conclusion

With all the procedures being correctly done, now the generated data can be arranged as follows:

├── train/
│   ├── sun3d-brown_bm_1-brown_bm_1/
│   ├── sun3d-brown_cogsci_1-brown_cogsci_1/
│   ├── ...
├── val/
│   ├── sun3d-brown_bm_4-brown_bm_4/
│   ├── sun3d-harvard_c11-hv_c11_2/
│   ├── ...
├── test/
│   ├── 7-scenes-redkitchen/
│   ├── 7-scenes-redkitchen-evaluation/
│   ├── ...
├── train_info.json
├── val_info.json

Download

train.zip (2.46G, pwd: rjzs)

val.zip (190M, pwd: 1pf9)

test.zip

train_info.json (overlap_ratio = 0.3)

val_info.json (overlap_ratio = 0.3)

Reference

3dmatch-toolbox

PPF-FoldNet

OverlapPredator