This project covers the LiDAR super-resolution task, but without the resolution constraint of a deep network. The Implicit LiDAR Network (ILN) reconstructs the dense LiDAR points based on the sparse sensor measurements, depending on the system's requirement. It can predict the detection distance of any continuous query laser within the sensor's field of view. You can see technical details on the project page.
The implementation is mainly based on the PyTorch framework for network training. Also, We use the ROS for 3D visualization and other robotic applications, but it can be optional.
The "requirements.txt" file includes the major Python packages we used. Check the dependencies or install them in your environment using the command:
pip3 install -r python_src/requirements.txt
Our package uses the Pybind11 library to call several C++ voxelization functions from Python code, and is included when cloning with the --recursive option: git clone git@github.com:PinocchioYS/iln.git --recursive
Use these commands to build the voxelization module in a ROS workspace:
cd <your_catkin_repo>
git clone git@github.com:PinocchioYS/iln.git --recursive src/iln
catkin_make
Use these command to build the voxelization module outside or a ROS workspace:
git clone git@github.com:PinocchioYS/iln.git --recursive mkdir build cd build cmake .. make
We collected the training dataset by LiDAR simulation using the CARLA. This dataset consists the 9-realistic outdoor scenes, and each scene has LiDAR data with four different resolutions: 16x1024, 64x1024, 128x2048, and 256x4096 (vertical x horizontal angles). You can download our dataset from the link or the following command.
cd python_src/dataset
wget https://sgvr.kaist.ac.kr/~yskwon/papers/icra22-iln/Carla.zip
unzip Carla.zip
These commands put the dataset into "python_src/dataset/Carla" which our example codes use as the default directory.
This project provides several models that we trained with the CARLA dataset: ILN [Ours, ICRA22], LIIF [CVPR21], and LiDAR-SR [RAS20]. You can download the model files from the link or the following commands.
cd python_src/models/trained
wget https://sgvr.kaist.ac.kr/~yskwon/papers/icra22-iln/trained.zip
unzip trained.zip
These commands make the pre-trained models into "python_src/models/trained" where our default model "iln_1d_400.pth" is located.
Visualization is supported with ROS in the implicit_lidar_network package:
usage: demo_resolution_free_lidar.py [-h] -i INPUT_FILENAME -cp CHECKPOINT [-v VOXEL_SIZE]
demo_resolution_free_lidar.py: error: the following arguments are required: -i/--input_filename, -cp/--checkpoint