MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries. Paper - Project Page

This repo implements the paper MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries. We built our implementation upon MMdetection3D.

The major part of the code is in the directory plugin/track. To use this code with MMDetection3D, we need older versions of MMDetection3D families(see Environment section), and you need to replace mmdet3d/api with the mmdet3d/api provided here.

How to run

Environment

First, install:

1. mmcv==1.3.14
2. mmdetection==2.12.0
3. nuscenses-devkit
4. Note: for tracking we need to install: motmetrics==1.1.3, not newer version, like motmetrics==1.2.0!!

Second, clone mmdetection3d==0.13.0, but replace its mmdet3d/api/ from mmdetection3d by mmdet3d/api/ in this repo.

e.g.

git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
git checkout v0.13.0
# cp -r ../mmdet3d/api mmdet3d/
# cp ../mmdet3d/models/builder.py mmdet3d/models/
# cp ../mmdet3d/models/detectors/mvx_two_stage.py mmdet3d/models/detectors/mvx_two_stage.py

# replace the mmdetection3d/mmdet3d with the mmdet3d_full
cp -r ../mmdet3d_full ./mmdet3d

cp -r ../plugin ./ 
cp -r ../tools ./ 
# then install mmdetection3d following its instruction. 
# and mmdetection3d becomes your new working directories. 

Dataset preprocessing

After preparing the nuScenes Dataset following mmdetection3d, you need to generate a meta file or say .pkl file.

python3 tools/data_converter/nusc_track.py

Run training

I provide a template config file in plugin/track/configs/resnet101_fpn_3frame.py. You can directly use this config or read this file, especially its comments, and modify whatever you want. I recommend using DETR3D pre-trained models or other nuScenes 3D Detection pre-trained models.

basic training scripts on a machine with 8 GPUS:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash tools/dist_train_tracker.sh plugin/track/configs/resnet101_fpn_3frame.py 8 --work-dir=work_dirs/experiment_name

basic test scripts

# You can perform inferece, then save the result file
python3 tools/test.py plugin/track/configs/resnet101_fpn_3frame.py <model-path> --format-only --eval-options jsonfile_prefix=<dir-name-for-saving-json-results>

# or you can perform inference and directly perform the evaluation
python3 tools/test.py plugin/track/configs/resnet101_fpn_3frame.py <model-path>  --eval --bbox

Visualization

For visualization, I suggest user to generate the results json file first. I provide some sample code at tools/nusc_visualizer.py for visualizing the predictions, see function _test_pred() in tools/nusc_visualize.py for examples.

Results

Backbones	AMOTA-val	AMOTP-val	IDS-val	Download
ResNet-101 w/ FPN	29.5	1.498	4388	model | val results
ResNet-50 w/ FPN	25.2	1.573	3899	model | val results

Acknowledgment

For the implementation, we rely heavily on MMCV, MMDetection, MMDetection3D,MOTR, and DETR3D

Relevant projects

1. DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
2. FUTR3D: A Unified Sensor Fusion Framework for 3D Detection
3. For more projects on Autonomous Driving, check out our camera-centered autonomous driving projects page webpage

Reference

@article{zhang2022mutr3d,
  title={MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries},
  author={Zhang, Tianyuan and Chen, Xuanyao and Wang, Yue and Wang, Yilun and Zhao, Hang},
  journal={arXiv preprint arXiv:2205.00613},
  year={2022}
}

Contact: Tianyuan Zhang at: tianyuaz@andrew.cmu.edu or tianyuanzhang1998@gmail.com