original github: https://github.com/epiception/CalibNet

original paper: CalibNet: Self-Supervised Extrinsic Calibration using 3D Spatial Transformer Networks

Many thanks to otaheri for providing the CUDA implementation of chamfer distance otaheri/chamfer_distance.

1.Recommended Environment

2.Dataset Preparation

3.Train and Test

Windows 10 / Ubuntu 18.04 / Ubuntu 20.04

Pytorch >= 1.8

CUDA 11.1

Python >= 3.8

conda create -n <env_name> python=3.8

conda activate <env_name>

Please note that a more recent pytorch is likely compatatible with our codes. If you did not install pytorch before, you can try the following command.

conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge

pip3 install -r requirements.txt

KITTI Odometry (You may need to register first to acquire access)

Download Link

Dataset Should be organized into data/ filefolder in our root:

/PATH/TO/CalibNet_pytorch/
  --|data/
      --|poses/
          --|00.txt
          --|01.txt
          --...
      --|sequences/
          --|00/
              --|image_2/
              --|image_3/
              --|velodyne/
              --|calib.txt
              --|times.txt
          --|01/
          --|02/
          --...
  --...

Use demo.py to check your data.

P0: 7.188560000000e+02 0.000000000000e+00 6.071928000000e+02 0.000000000000e+00 0.000000000000e+00 7.188560000000e+02 1.852157000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00
P1: 7.188560000000e+02 0.000000000000e+00 6.071928000000e+02 -3.861448000000e+02 0.000000000000e+00 7.188560000000e+02 1.852157000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00
P2: 7.188560000000e+02 0.000000000000e+00 6.071928000000e+02 4.538225000000e+01 0.000000000000e+00 7.188560000000e+02 1.852157000000e+02 -1.130887000000e-01 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 3.779761000000e-03
P3: 7.188560000000e+02 0.000000000000e+00 6.071928000000e+02 -3.372877000000e+02 0.000000000000e+00 7.188560000000e+02 1.852157000000e+02 2.369057000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 4.915215000000e-03
Tr: 4.276802385584e-04 -9.999672484946e-01 -8.084491683471e-03 -1.198459927713e-02 -7.210626507497e-03 8.081198471645e-03 -9.999413164504e-01 -5.403984729748e-02 9.999738645903e-01 4.859485810390e-04 -7.206933692422e-03 -2.921968648686e-01

cd /PATH/TO/CalibNet_pytorch
ln -s /PATH/TO/MyData/dataset data

The following command is fit with a 12GB GPU.

python train.py --batch_size=8 --epoch=100 --inner_iter=1 --pcd_sample=4096 --name=cam2_oneiter --skip_frame=10

python test.py --inner_iter=1 --pretrained=./checkpoint/cam2_oneiter_best.pth --skip_frame=1 --pcd_sample=-1

Download pretrained cam2_oneiter_best.pth from here and put it into root/checkpoint/.

pcd_sample=-1 means input the whole point cloud (but random permuted). However, you need to keep batch_size=1 accordingly, or it may cause collation error for dataloader.

Relevant training logs can be found in log dir.

Rotation (deg) X:3.0023,Y:2.9971,Z:3.0498

Translation (m): X:0.0700,Y:0.0673,Z:0.0862

see config.yml for dataset setting.

dataset:
  train: [0,1,2,3,4,5,6,7]
  val: [8,9,10]
  test: [11,12,13]
  cam_id: 2  # (2 or 3)
  pooling: 3 # max pooling of semi-dense image, must be odd

• KITTI Odometry has 22 sequences, and you need to split them into three categories for training, validation and testing in config.yml.

• cam_id=2 represents left color image dataset and cam_id=3 represents the right.

• set pooling paramter (only support odd numbers) to change max pooling of preprocessing for depth map.