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[arXiv]
[bibtex]@InProceedings{Qiu_2025_WACV, author = {Qiu, Wenzhao and Pang, Shanmin and Zhang, Hao and Fang, Jianwu and Xue, Jianru}, title = {HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {6022-6031} }
HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning
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 https://github.com/adasfag/HeightMapNet/.
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