You can install LCDNet locally on your machine, or use the provided Dockerfile to run it in a container. The environment_lcdnet.yml file is meant to be used with docker, as it contains version of packages that are specific to a CUDA version. We don't recommend using it for local installation.
- Install PyTorch (make sure to select the correct cuda version)
- Install the requirements
pip install -r requirements.txt - Install spconv <= 2.1.25 (make sure to select the correct cuda version, for example
pip install spconv-cu113==2.1.25for cuda 11.3) - Install OpenPCDet
- Install faiss-cpu - NOTE: avoid installing faiss via pip, use the conda version, or build it from source alternatively.
conda install -c pytorch faiss-cpu==1.7.3
Tested in the following environments:
- Ubuntu 18.04/20.04/22.04
- Python 3.7
- cuda 10.2/11.1/11.3
- pytorch 1.8/1.9/1.10
- Open3D 0.12.0/0.14.1
Download the KITTI-360 dataset (raw velodyne scans, calibrations and vehicle poses) and generate the loop ground truths:
python -m data_process.generate_loop_GT_KITTI360 --root_folder KITTI360_ROOT
where KITTI360_ROOT is the path where you downloaded and extracted the KITTI-360 dataset.
To achieve better results, it is suggested to preprocess the datasets by removing the ground plane:
python -m data_process.remove_ground_plane_kitti --root_folder KITT_ROOT
python -m data_process.remove_ground_plane_kitti360 --root_folder KITT_ROOT360
If you skip this step, please remove the option --without_ground in all the following steps.
The training script will use all the available GPUs, if you want to use only a subset of the GPUs, use the environment variable CUDA_VISIBLE_DEVICES. If you don't know how to do that, check here.
To train on the KITTI dataset:
python -m training_KITTI_DDP --root_folder KITTI_ROOT --dataset kitti --batch_size B --without_ground
To train on the KITTI-360 dataset:
python -m training_KITTI_DDP --root_folder KITTI360_ROOT --dataset kitti360 --batch_size B --without_ground
To track the training progress using Weights & Biases, add the argument --wandb.
The per-GPU batch size B must be at least 2, and a GPU with at least 8GB of RAM is required (12GB or more is preferred).
The network's weights will be saved in the folder ./checkpoints (you can change this folder with the argument --checkpoints_dest), inside a subfolder named with the starting date and time of the training (format %d-%m-%Y_%H-%M-%S), for example: 20-02-2022_16-38-24
To evaluate the loop closure performance of the trained model on the KITTI dataset:
python -m evaluation.inference_loop_closure --root_folder KITTI_ROOT --dataset kitti --validation_sequence 08 --weights_path WEIGHTS --without_ground
where WEIGHTS is the path of the pretrained model, for example ./checkpoints/20-02-2022_16-38-24/checkpoint_last_iter.tar
Similarly, on the KITTI-360 dataset:
python -m evaluation.inference_loop_closure --root_folder KITTI360_ROOT --dataset kitti360 --validation_sequence 2013_05_28_drive_0002_sync --weights_path WEIGHTS --without_ground
To evaluate the loop closure performance of the trained model on the KITTI and KITTI-360 dataset:
python -m evaluation.inference_yaw_general --root_folder KITTI_ROOT --dataset kitti --validation_sequence 08 --weights_path WEIGHTS --ransac --without_ground
python -m evaluation.inference_yaw_general --root_folder KITTI360_ROOT --dataset kitti360 --validation_sequence 2013_05_28_drive_0002_sync --weights_path WEIGHTS --ransac --without_ground
To evaluate LCDNet (fast), remove the --ransac argument.
A model pretrained on the KITTI-360 dataset can be found here