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/.