HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning

• URL: http://arxiv.org/abs/2411.01408v1
• Date: Sun, 03 Nov 2024 02:35:17 GMT
• Title: HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning
• Authors: Wenzhao Qiu, Shanmin Pang, Hao zhang, Jianwu Fang, Jianru Xue,
• Abstract summary: We introduce HeightMapNet, a novel framework that establishes a dynamic relationship between image features and road surface height distributions. Our approach refines the accuracy of Bird's-Eye-View (BEV) features beyond conventional methods. HeightMapNet has shown exceptional results on the challenging nuScenes and Argoverse 2 datasets.
• Score: 22.871397412478274
• License:
• Abstract: Recent advances in high-definition (HD) map construction from surround-view images have highlighted their cost-effectiveness in deployment. However, prevailing techniques often fall short in accurately extracting and utilizing road features, as well as in the implementation of view transformation. In response, we introduce HeightMapNet, a novel framework that establishes a dynamic relationship between image features and road surface height distributions. By integrating height priors, our approach refines the accuracy of Bird's-Eye-View (BEV) features beyond conventional methods. HeightMapNet also introduces a foreground-background separation network that sharply distinguishes between critical road elements and extraneous background components, enabling precise focus on detailed road micro-features. Additionally, our method leverages multi-scale features within the BEV space, optimally utilizing spatial geometric information to boost model performance. HeightMapNet has shown exceptional results on the challenging nuScenes and Argoverse 2 datasets, outperforming several widely recognized approaches. The code will be available at \url{https://github.com/adasfag/HeightMapNet/}.

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