通过显式建模道路高度，提升端到端高精地图生成精度
HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning
- 引入图像特征与路面高程分布的动态关联机制
- 在nuScenes和Argoverse 2上超越多个主流方法
- 适合自动驾驶高精地图构建与视觉几何建模研究者
从环视图像构建高精度（HD）地图的最新进展表明其部署成本更低。然而，现有方法在提取和利用道路特征、以及视图变换实现方面仍存在不足。为此，我们提出HeightMapNet，一种建立图像特征与道路表面高程分布动态关系的新框架。通过引入高程先验，该方法显著提升了鸟瞰图（BEV）特征的准确性。HeightMapNet还设计了前景-背景分离网络，精准区分关键道路要素与无关背景，聚焦道路细微结构。此外，方法在BEV空间中融合多尺度特征，有效利用空间几何信息以增强性能。在挑战性数据集nuScenes和Argoverse 2上，HeightMapNet表现优异，优于多个广泛认可的方法。代码将公开于\url{https://github.com/adasfag/HeightMapNet/}。
原文摘要 · Abstract (English)
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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