TransFusion repository

PyTorch implementation of TransFusion for CVPR'2022 paper "TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers", by Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu and Chiew-Lan Tai.

This paper focus on LiDAR-camera fusion for 3D object detection. If you find this project useful, please cite:

@article{bai2021pointdsc,
  title={{TransFusion}: {R}obust {L}iDAR-{C}amera {F}usion for {3}D {O}bject {D}etection with {T}ransformers},
  author={Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu and Chiew-Lan Tai},
  journal={CVPR},
  year={2022}
}

Introduction

LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against inferior image conditions, e.g., bad illumination and sensor misalignment, is under-explored. Existing fusion methods are easily affected by such conditions, mainly due to a hard association of LiDAR points and image pixels, established by calibration matrices. We propose TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions. Specifically, our TransFusion consists of convolutional backbones and a detection head based on a transformer decoder. The first layer of the decoder predicts initial bounding boxes from a LiDAR point cloud using a sparse set of object queries, and its second decoder layer adaptively fuses the object queries with useful image features, leveraging both spatial and contextual relationships. The attention mechanism of the transformer enables our model to adaptively determine where and what information should be taken from the image, leading to a robust and effective fusion strategy. We additionally design an image-guided query initialization strategy to deal with objects that are difficult to detect in point clouds. TransFusion achieves state-of-the-art performance on large-scale datasets. We provide extensive experiments to demonstrate its robustness against degenerated image quality and calibration errors. We also extend the proposed method to the 3D tracking task and achieve the 1st place in the leaderboard of nuScenes tracking, showing its effectiveness and generalization capability.

updates

• March 23, 2022: paper link added
• March 15, 2022: initial release

Main Results

Detailed results can be found in nuscenes.md and waymo.md. Configuration files and guidance to reproduce these results are all included in configs, we are not going to release the pretrained models due to the policy of Huawei IAS BU.

nuScenes detection test

Model	Backbone	mAP	NDS	Link
TransFusion-L	VoxelNet	65.52	70.23	Detection
TransFusion	VoxelNet	68.90	71.68	Detection

nuScenes tracking test

Model	Backbone	AMOTA	AMOTP	Link
TransFusion-L	VoxelNet	0.686	0.529	Detection / Tracking
TransFusion	VoxelNet	0.718	0.551	Detection / Tracking

waymo detection validation

Model	Backbone	Veh_L2	Ped_L2	Cyc_L2	MAPH
TransFusion-L	VoxelNet	65.07	63.70	65.97	64.91
TransFusion	VoxelNet	65.11	64.02	67.40	65.51

Use TransFusion

Installation

Please refer to getting_started.md for installation of mmdet3d. We use mmdet 2.10.0 and mmcv 1.2.4 for this project.

Benchmark Evaluation and Training

Please refer to data_preparation.md to prepare the data. Then follow the instruction there to train our model. All detection configurations are included in configs.

Note that if you a the newer version of mmdet3d to prepare the meta file for nuScenes and then train/eval the TransFusion, it will have a wrong mAOE and mASE because mmdet3d has a coordinate system refactoring which affect the definitation of yaw angle and object size (l, w).

Acknowlegement

We sincerely thank the authors of mmdetection3d, CenterPoint, GroupFree3D for open sourcing their methods.