通过结构化训练融合空地视角，提升实时高精地图构建精度
AerialFusionMapNet: Online HD Map Construction with Aerial-Onboard BEV Fusion
- 采用两阶段结构化训练，增强空中图像的结构先验信息
- 在nuScenes地理划分上达到54.7 mAP，较基线提升5.9绝对值
- 适合关注空地融合与高精地图构建的研究者与开发者
高分辨率航拍影像作为自动驾驶感知的补充模态，与车载传感器融合后可提升鸟瞰图场景理解能力。以往工作虽在在线高精地图构建中验证了空地融合的有效性，但传统端到端融合未能充分挖掘航拍表征中的结构信息。本文提出AerialFusionMapNet，一种基于结构化两阶段训练策略的融合映射框架，显式增强空中特征在统一流程中的贡献。该训练方案有效整合了结构化空中先验。在nuScenes地理划分数据集上，AerialFusionMapNet达到54.7 mAP，相比先前空地融合基线48.8 mAP，绝对提升5.9，相对提升12.1%。结果表明，结构化训练设计比模型复杂度增加更能释放航拍影像在在线高精地图构建中的潜力。代码与训练模型已开源。
原文摘要 · Abstract (English)
High-resolution aerial imagery has recently emerged as a complementary modality for automated driving perception and has shown potential to improve birds-eye-view (BEV) scene understanding when fused with onboard sensors. Prior work demonstrated performance gains for online high-definition (HD) map construction through aerial-onboard fusion; however, conventional end-to-end fusion does not fully exploit the structural information contained in aerial representations. In this work, we introduce AerialFusionMapNet, a fusion-based mapping framework with a structured two-stage training strategy that explicitly enhances the contribution of aerial features within a unified pipeline. The proposed training scheme enables more effective integration of structural aerial priors. On the nuScenes geographic split, AerialFusionMapNet achieves up to 54.7 mAP, improving over prior aerial-onboard fusion baselines from 48.8 mAP by +5.9 absolute and +12.1% relative. The results suggest that structured training design, rather than increased architectural complexity, plays a more decisive role in unlocking the full potential of aerial imagery for online HD map construction. Code and trained models are available at https://github.com/DriverlessMobility/AerialFusionMapNet.
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