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HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction
March 14, 2024, 4:46 a.m. | Yi Zhou, Hui Zhang, Jiaqian Yu, Yifan Yang, Sangil Jung, Seung-In Park, ByungIn Yoo
cs.CV updates on arXiv.org arxiv.org
Abstract: Vectorized High-Definition (HD) map construction requires predictions of the category and point coordinates of map elements (e.g. road boundary, lane divider, pedestrian crossing, etc.). State-of-the-art methods are mainly based on point-level representation learning for regressing accurate point coordinates. However, this pipeline has limitations in obtaining element-level information and handling element-level failures, e.g. erroneous element shape or entanglement between elements. To tackle the above issues, we propose a simple yet effective HybrId framework named HIMap to …
abstract art arxiv construction cs.cv definition etc however hybrid limitations map pedestrian pipeline predictions representation representation learning state type
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