@inproceedings{
zhu2024icmapper,
title={{IC}-Mapper: Instance-Centric Spatio-Temporal Modeling for Online Vectorized Map Construction},
author={Jiangtong Zhu and YangZhao and Yinan Shi and Jianwu Fang and Jianru Xue},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=DeN82P7RIl}
}
Online vector map construction based on visual data can bypass the processes of data collection, post-processing, and manual annotation required by traditional map construction, which significantly enhances map-building efficiency. However, existing work treats the online mapping task as a local range perception task, overlooking the spatial scalability required for map construction. We propose \emph{IC-Mapper}, an instance-centric online mapping framework, which comprises two primary components: 1) \textbf{Instance-centric temporal association module:} For the detection queries of adjacent frames, we measure them in both feature and geometric dimensions to obtain the matching correspondence between instances across frames. 2) \textbf{Instance-centric spatial fusion module:} We perform point sampling on the historical global map from a spatial dimension and integrate it with the detection results of instances corresponding to the current frame to achieve real-time expansion and update of the map. Based on the nuScenes dataset, we evaluate our approach on detection, tracking, and global mapping metrics. Experimental results demonstrate the superiority of IC-Mapper against other state-of-the-art methods.
IC-Mapper: Instance-Centric Spatio-Temporal Modeling for Online Vectorized Map Construction
Jiangtong Zhu YangZhao Yinan Shi Jianwu Fang Jianru Xue