LiRaFusion - Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection

Paper

News

• (2024/5) We have released the codebase of LiRaFusion! Please check the readme document for the usage.
• (2024/1) LiRaFusion is accepted to ICRA 2024! See you in Yokohama, Japan!

Abstract

We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors. To improve the feature extraction capabilities from these two modalities, we design an early fusion module for joint voxel feature encoding, and a middle fusion module to adaptively fuse feature maps via a gated network. We perform extensive evaluation on nuScenes to demonstrate that LiRaFusion leverages the complementary information of LiDAR and radar effectively and achieves notable improvement over existing methods.

Results

Our method achieves state-of-the-art LiDAR-radar detection performance on the nuScenes dataset. We show a table of a comparison of LiRaFusion (using TransHead) with other methods below. Please feel free to check more results in our paper.

Method	Sensor	NDS $\uparrow$	mAP $\uparrow$	Car $\uparrow$	Ped $\uparrow$	Bicycle $\uparrow$	Bus $\uparrow$	Barrier $\uparrow$	TC $\uparrow$	Truck $\uparrow$	Trailer $\uparrow$	Moto $\uparrow$	CV $\uparrow$
FUTR3D-LO	LO	65.74	59.39	84.3	81.4	49.0	65.4	62.4	64.2	53.5	41.8	66.4	25.5
FUTR3D-LR	LR	65.37	58.08	83.8	81.2	49.8	65.4	60.4	60.6	51.0	41.0	65.1	22.6
EZFusion-LR$^*$	LR	65.77	59.24	84.6	81.7	47.3	69.1	62.0	65.7	52.2	39.7	66.9	23.3
LiRaFusion (ours)	LR	66.69	60.11	85.6	82.2	46.9	69.6	61.2	66.0	54.0	40.7	68.1	26.7

Usage

Prerequisites

We recommend setting up your environment following ours. We list the main dependencies below. Please note we use version 1.x of MMdetection3D, which is after the coordinate system refactoring. We build LiRaFusion based on the codebase of FUTR3D. Please refer to the original FUTR3D repo for more details and steps to set up the baseline environment.

We list our environment setup below:

• Python 3.8
• PyTorch 1.12.1
• TorchVision 0.13.1
• CUDA 11.3
• CuDNN 8.3.2
• OpenCV 4.9.0
• MMCV 1.7.0
• MMCV Compiler GCC 9.3
• MMCV CUDA Compiler 11.3
• MMDetection 2.28.2
• MMSegmentation 0.30.0
• MMDetection3D 1.0.0rc4+49de750

Known Issues

You might encounter some issues during the first run. We list some common issues and solutions below:

https://stackoverflow.com/questions/74947992/how-to-remove-the-error-systemerror-initialization-of-internal-failed-without

open-mmlab/mmdetection#10962

Data Preparation

To prepare the dataset and ensure the data (radar) is correctly loaded, please follow the instructions given in MMDetection3D. Please use the file provided in tools/data_converter/radar_converter_coordinate_v1.py to generate the corresponding data instead.

Please also make sure you edit the data_root directory in plugin/_base_/datasets/nus-3d.py and plugin/lirafusion/configs/lidar_radar/lidar_radar_lirafusion_futr3d.py to point to the correct data directory. We highlight the entry using TODO flag in the file.

Training

To train LiRaFusion with 8 GPUs, you can use the following command:

bash tools/dist_train.sh plugin/lirafusion/configs/lidar_radar/lidar_radar_lirafusion_futr3d.py 8

Evaluation

To evaluate the trained model with 2 GPUs (the same setup as ours), you can use the following command:

bash tools/dist_test.sh plugin/lirafusion/configs/lidar_radar/lidar_radar_lirafusion_futr3d.py work_dirs/lidar_radar_lirafusion_futr3d/latest.pth 2 --eval mAP

Please modify the pth file path to the path of your trained model. You can download our trained model from this link

Citation

If LiRaFusion is useful or relevant to your research, please kindly recognize our contributions by citing our paper:

@inproceedings{song2024lirafusion,
  author    = {Song, Jingyu and Zhao, Lingjun and Skinner, Katherine A},
  title     = {LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection},
  journal   = {2024 IEEE International Conference on Robotics and Automation (ICRA)},
  year      = {2024},
}