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LiT: LiDAR Translator

Project Page | Source Code | Paper

LiT (LiDAR Translator), is a pioneering framework aimed at bridging the domain gaps in LiDAR data from various devices and environments. It facilitates the unification of diverse LiDAR datasets, enhancing scalability and coherence in perception models. Our system includes advanced scene modeling, realistic LiDAR simulation, and a GPU-accelerated ray casting engine, enabling cutting-edge zero-shot domain detection. Explore LiT and see our interactive demos at our project page, and check out our paper for more details.

This project is built with CamTools.

Built with CamTools

Code Structure

# To get an overview of LiT, we recommend starting from the containers:
lit
├── containers                # Container classes for LiT
│   ├── base_container.py     # Base abstract container class
│   ├── fg_box.py             # Containing one foreground bounding box
│   ├── fg_object.py          # Containing one foreground object with multiple bounding boxes
│   ├── fg_scene.py           # Containing multiple foreground objects
│   ├── frame.py              # Containing one frame of the original data
│   ├── scene.py              # Containing multiple frames of the original data
│   ├── sim_frame.py          # Containing one frame of the simulated data
│   └── sim_scene.py          # Containing multiple frames of the simulated data

# The followings are the other modules of LiT:
├── ext                       # Compiled extensions
│   ├── __init__.py
│   └── lit_ext
├── extern                    # External modules
│   ├── deepsdf
│   ├── otk-pyoptix
│   └── spconv
├── bg_reconstructor.py       # Background reconstructor
├── fg_reconstructor.py       # Foreground reconstructor
├── __init__.py
├── lidar.py                  # LiDAR parameters
├── lidar_utils.py            # LiDAR utilities
├── path_utils.py             # Defines the data paths for LiT (important)
├── raycast_engine_cpu.py     # ─┐
├── raycast_engine_gpu.cu     #  │
├── raycast_engine_gpu.py     #  ├─> LiT's GPU OptiX raycast engine
├── raycast_engine.py         #  │
├── vector_math.h             # ─┘
├── recon_utils.py            # Reconstruction utilities
└── utils.py                  # General utilities

# The followings are the "driver code", you run these scripts to use LiT:
lit_tools
├── global_configs.py
├── lit_01_extract_scene.py   # Extract scenes to LiT's format
├── lit_02_recon_bg.py        # Reconstruct background
├── lit_03_recon_fg.py        # Reconstruct foreground
├── lit_04_sim.py             # Run simulations
├── lit_raykeep_dataset.py    # ─┐
├── lit_train_mlp.py          #  ├─> Ray drop related
└── lit_train_raykeep.py      # ─┘

Installation

# conda env
conda create -n lit python=3.10
conda activate lit

# torch
pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118

# additional dependencies from pip
pip install waymo-open-dataset-tf-2-11-0==1.6.0
pip install nuscenes-devkit==1.0.5

# nksr (python-pycg[full] includes open3d)
pip install nksr -f https://nksr.huangjh.tech/whl/torch-2.0.0+cu118.html
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu118.html
pip install python-pycg[full] -f https://pycg.s3.ap-northeast-1.amazonaws.com/packages/index.html
python -c "import nksr"

# extern: spconv
cd lit/extern/spconv
rm -rf build
python setup.py bdist_wheel
pip install dist/spconv-1.2.1-cp310-cp310-linux_x86_64.whl
cd ../../..

# extern: otk-pyoptix
cd lit/extern/otk-pyoptix
pip install cupy-cuda11x numpy pillow pynvrtc pytest
rm -rf optix/build
PYOPTIX_CMAKE_ARGS="-DOptiX_INSTALL_DIR=${PWD}/sdk/NVIDIA-OptiX-SDK-7.4.0-linux64-x86_64" pip install -e optix
cd ../../..

# lit
pip install -r requirements.txt
pip install -e .

# fixes, as waymo-open-dataset has unnecessarily strict version requirements
pip install numpy==1.22 protobuf==3.20.0

Dataset

KITTI Dataset

Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows.

data/kitti
│── training
│   └── calib & velodyne & label_2 & image_2
└── testing
    └── calib & velodyne & image_2

Then, run the pre-processing scripts

python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos \
    tools/cfgs/dataset_configs/kitti_dataset.yaml

After pre-processing, the directory layout should look like this:

tree -lL 1 data/kitti

data/kitti
├── gt_database
├── kitti_dbinfos_train.pkl
├── kitti_infos_test.pkl
├── kitti_infos_train.pkl
├── kitti_infos_trainval.pkl
├── kitti_infos_val.pkl
├── testing
└── training

nuScenes Dataset

Please download the official NuScenes 3D object detection dataset and organize the downloaded files as follows.

data/nuscenes
└── v1.0-trainval
    │── samples
    │── sweeps
    │── maps
    └── v1.0-trainval

Then, run the pre-processing scripts

python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos \
    --cfg_file tools/cfgs/dataset_configs/nuscenes_dataset.yaml \
    --version v1.0-trainval

After pre-processing, the directory layout should look like this:

tree -lL 2 data/nuscenes

data/nuscenes
└── v1.0-trainval
    ├── gt_database_10sweeps_withvelo
    ├── maps
    ├── nuscenes_dbinfos_10sweeps_withvelo.pkl
    ├── nuscenes_infos_10sweeps_train.pkl
    ├── nuscenes_infos_10sweeps_val.pkl
    ├── samples
    ├── sweeps
    ├── v1.0-test
    └── v1.0-trainval

Waymo Dataset

Please download the official Waymo Open Dataset, including the training data training_0000.tar~training_0031.tar and the validation data validation_0000.tar~validation_0007.tar. Unzip all the above xxxx.tar files to the directory of data/waymo/raw_data as follows (You could get 798 train tfrecord and 202 val tfrecord).

data/waymo
│── raw_data
│   │── segment-xxxxxxxx.tfrecord
│   │── ...
│── waymo_processed_data
│   │── segment-xxxxxxxx/
│   │── ...
│── pcdet_gt_database_train_sampled_xx/
└── pcdet_waymo_dbinfos_train_sampled_xx.pkl
···

Then, run the pre-processing scripts

python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
    --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml --workers 10

After pre-processing, the directory layout should look like this:

tree -lL 1 data/waymo

data/waymo
├── pcdet_gt_database_train_sampled_10
├── pcdet_waymo_dbinfos_train_sampled_10.pkl
├── raw_data
├── waymo_infos_train_extra.pkl
├── waymo_infos_train.pkl
├── waymo_infos_val_extra.pkl
├── waymo_infos_val.pkl
├── waymo_processed_data
└── waymo_processed_data_extra

LiT Usage

  1. Extract scenes

    cd lit_tools
python lit_01_extract_scene.py --cfg_file ../tools/cfgs/dataset_configs/waymo_dataset_extract.yaml --split train
python lit_01_extract_scene.py --cfg_file ../tools/cfgs/dataset_configs/waymo_dataset_extract.yaml --split valid
python lit_01_extract_scene.py --cfg_file ../tools/cfgs/dataset_configs/nuscenes_dataset_extract.yaml --split train
python lit_01_extract_scene.py --cfg_file ../tools/cfgs/dataset_configs/nuscenes_dataset_extract.yaml --split valid

  2. Reconstruct background

    cd lit_tools
python lit_02_recon_bg.py --data_domain waymo --data_version v0 --skip_existing
python lit_02_recon_bg.py --data_domain nuscenes --data_version v0 --skip_existing

  3. Reconstruct foreground

    cd lit_tools
python lit_03_recon_fg.py --data_domain waymo --data_version v0 --skip_existing
python lit_03_recon_fg.py --data_domain nuscenes --data_version v0 --skip_existing

  4. Run simulations

    cd lit_tools

# Waymo -> KITTI
python lit_04_sim.py --src_style waymo --dst_style kitti --skip_existing --data_version v0

# Waymo -> nuScenes
python lit_04_sim.py --src_style waymo --dst_style nuscenes --skip_existing --data_version v0

# Waymo -> Waymo (self translation)
python lit_04_sim.py --src_style waymo --dst_style waymo --skip_existing --data_version v0

# nuScenes -> KITTI
python lit_04_sim.py --src_style nuscenes --dst_style kitti --skip_existing --data_version v0

# nuScenes -> nuScenes (self translation)
python lit_04_sim.py --src_style nuscenes --dst_style nuscenes --skip_existing --data_version v0

Training and Testing

The training and testing steps are largely the same as ST3D which are based on OpenPCDet.

First, you'll need to train "source-only" models which are purely trained on the source domain data. You shall follow the instructions in the ST3D documentation.

Then, you can train domain unification models. All you'll need to do is point to the correct configuration file and the correct checkpoint file. We give a few examples:

cd tools

# Waymo -> KITTI, PV-RCNN
python train.py --cfg_file cfgs/da-waymo-kitti_models/pvrcnn/pvrcnn_old_anchor_ft_v1.yaml --pretrained_model source_only/waymo_kitti_pvrcnn.pth --batch_size 16 --lr 0.01 --epochs 10

# nuScenes -> KITTI, SECOND-IoU
python train.py --cfg_file cfgs/da-nuscenes-kitti_models/secondiou/secondiou_old_anchor_ft_v1.yaml --pretrained_model ours_source_only/nuscenes_kitti_second.pth --batch_size 32 --lr 0.003 --epochs 10

Notes:

  • After each epoch of training the domain unification model, the model will be automatically evaluated on the target test set.
  • Typically, it takes less than 10 epochs to reach the best performance, you may pick the model with the best performance on the target test set.