MachMap: End-to-End Vectorized Solution for Compact HD-Map Construction
Limeng Qiao Yongchao Zheng Peng Zhang Wenjie Ding Xi Qiu Xing Wei Chi Zhang
This report introduces the 1st place winning solution for the Autonomous
Driving Challenge 2023 - Online HD-map Construction. By delving into the
vectorization pipeline, we elaborate an effective architecture, termed as
MachMap, which formulates the task of HD-map construction as the point
detection paradigm in the bird-eye-view space with an end-to-end manner.
Firstly, we introduce a novel map-compaction scheme into our framework, leading
to reducing the number of vectorized points by 93% without any expression
performance degradation. Build upon the above process, we then follow the
general query-based paradigm and propose a strong baseline with integrating a
powerful CNN-based backbone like InternImage, a temporal-based instance decoder
and a well-designed point-mask coupling head. Additionally, an extra optional
ensemble stage is utilized to refine model predictions for better performance.
Our MachMap-tiny with IN-1K initialization achieves a mAP of 79.1 on the
Argoverse2 benchmark and the further improved MachMap-huge reaches the best mAP
of 83.5, outperforming all the other online HD-map construction approaches on
the final leaderboard with a distinct performance margin (> 9.8 mAP at least).