• Aug. 31th, 2023: initial MapTRv2 is released at maptrv2 branch. Please run git checkout maptrv2 to use it.
• Aug. 14th, 2023: As required by many researchers, the code of MapTR-based map annotation framework (VMA) will be released at https://github.com/hustvl/VMA recently.
• Aug. 10th, 2023: We release MapTRv2 on Arxiv. MapTRv2 demonstrates much stronger performance and much faster convergence. To better meet the requirement of the downstream planner (like PDM), we introduce an extra semantic——centerline (using path-wise modeling proposed by LaneGAP). Code & model will be released in late August. Please stay tuned!
• May. 12th, 2023: MapTR now support various bevencoder, such as BEVFormer encoder and BEVFusion bevpool. Check it out!
• Apr. 20th, 2023: Extending MapTR to a general map annotation framework (paper, code), with high flexibility in terms of spatial scale and element type.
• Mar. 22nd, 2023: By leveraging MapTR, VAD (paper, code) models the driving scene as fully vectorized representation, achieving SoTA end-to-end planning performance!
• Jan. 21st, 2023: MapTR is accepted to ICLR 2023 as Spotlight Presentation!
• Nov. 11st, 2022: We release an initial version of MapTR.
• Aug. 31st, 2022: We released our paper on Arxiv. Code/Models are coming soon. Please stay tuned! ☕️

High-definition (HD) map provides abundant and precise static environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system. In this paper, we present Map TRansformer, an end-to-end framework for online vectorized HD map construction. We propose a unified permutation-equivalent modeling approach, i.e., modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process. We design a hierarchical query embedding scheme to flexibly encode structured map information and perform hierarchical bipartite matching for map element learning. To speed up convergence, we further introduce auxiliary one-to-many matching and dense supervision. The proposed method well copes with various map elements with arbitrary shapes. It runs at real-time inference speed and achieves state-of-the-art performance on both nuScenes and Argoverse2 datasets. Abundant qualitative results show stable and robust map construction quality in complex and various driving scenes.

Results from the MapTRv2 paper

Notes:

• FPS is measured on NVIDIA RTX3090 GPU with batch size of 1 (containing 6 view images).
• All the experiments are performed on 8 NVIDIA GeForce RTX 3090 GPUs.

Results from this repo.

Please git checkout maptrv2 and follow the install instruction to use following checkpoint

Notes:

• * means that we introduce an extra semantic——centerline (using path-wise modeling proposed by LaneGAP).

Youtube

Youtube

• Installation
• Prepare Dataset (Notes: annotation generation of MapTRv2 is different from MapTR )
• Train and Eval
• Visualization

• centerline detection & topology support
• multi-modal checkpoints
• multi-modal code
• lidar modality code
• argoverse2 dataset
• Nuscenes dataset
• MapTR checkpoints
• MapTR code
• Initialization

MapTR is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: BEVFusion, BEVFormer, HDMapNet, GKT, VectorMapNet.

If you find MapTR is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

@inproceedings{MapTR,
  title={MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction},
  author={Liao, Bencheng and Chen, Shaoyu and Wang, Xinggang and Cheng, Tianheng, and Zhang, Qian and Liu, Wenyu and Huang, Chang},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

@inproceedings{MapTRv2,
  title={MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction},
  author={Liao, Bencheng and Chen, Shaoyu and Zhang, Yunchi and Jiang, Bo and Zhang, Qian and Liu, Wenyu and Huang, Chang and Wang, Xinggang},
  booktitle={arXiv preprint arXiv: 2308.05736},
  year={2023}
}