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MoMA-M3T

Delving into Motion-Aware Matching for Monocular 3D Object Tracking (ICCV 2023) [paper]
Kuan-Chih Huang, Ming-Hsuan Yang, Yi-Hsuan Tsai.

<img src="resources/arch.png" alt="vis" style="zoom:50%;" />

Setup

Please refer to SETUP.md for installation and data preparation. Download checkpoints and detections here to root folder.

nuScenes Dataset

To evaluate on the validation set:

sh infer_eval_nusc_mini.sh  #for mini set
sh infer_eval_nusc_val.sh  #for val set

KITTI Dataset

To evaluate on the subval set (for 01,04,11,12,13,14,15,18 sequences):

sh infer_kitti_subval.sh  #inference
python ab3dmot_kitti/evaluate.py moma 1 3D 0.25  #evaluation

Acknowlegment

Our codes are mainly based on QD-3DT, and the evaluation code for KITTI dataset is from AB3DMOT. Thanks for their contributions.

Citation

@inproceedings{huang2023momam3t,
   author = {Kuan-Chih Huang, Ming-Hsuan Yang and Yi-Hsuan Tsai},
   title = {Delving into Motion-Aware Matching for Monocular 3D Object Tracking},
   booktitle = {ICCV},
   year = {2023}    
}