通过增强鸟瞰图特征，提升高精地图向量化精度。
AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Construction
- 在鸟瞰图隐空间引入网格增强，改善特征结构
- nuScenes上60米范围内提升13.3%的向量化地图精度
- 适用于实时高精地图构建，易集成于现有模型
自动驾驶需实时理解道路元素（如车道、人行横道），并以向量化形式表示。现有方法多采用学习的鸟瞰图（BEV）编码器融合多视角图像生成联合隐空间表示。传统方式通过预测密集栅格图提供空间监督，再经后处理转为向量；近期模型则直接输出多段线，实现实例级信息提取。本文提出增强地图网络（AugMapNet），创新性地引入隐式鸟瞰图特征网格增强技术，显著优化了隐空间表征。该方法更高效融合向量解码与密集空间监督，相比现有混合架构更易集成。实验在nuScenes和Argoverse2数据集上验证，60米范围内较StreamMapNet基线提升13.3%，更大范围性能优势更明显。将方法迁移至SQD-MapNet也获得相似提升。对隐空间的详细分析表明，AugMapNet具备更结构化的特征表示，证明其价值不仅在于性能提升。代码开源：https://github.com/tmonnin/augmapnet。
原文摘要 · Abstract (English)
Autonomous driving requires understanding infrastructure elements, such as lanes and crosswalks. To navigate safely, this understanding must be derived from sensor data in real-time and needs to be represented in vectorized form. Learned Bird's-Eye View (BEV) encoders are commonly used to combine a set of camera images from multiple views into one joint latent BEV grid. Traditionally, from this latent space, an intermediate raster map is predicted, providing dense spatial supervision but requiring post-processing into the desired vectorized form. More recent models directly derive infrastructure elements as polylines using vectorized map decoders, providing instance-level information. Our approach, Augmentation Map Network (AugMapNet), proposes latent BEV feature grid augmentation, a novel technique that significantly enhances the latent BEV representation. AugMapNet combines vector decoding and dense spatial supervision more effectively than existing architectures while remaining easy to integrate compared to other hybrid approaches. It additionally benefits from extra processing on its latent BEV features. Experiments on nuScenes and Argoverse2 datasets demonstrate significant improvements on vectorized map prediction of up to 13.3% over the StreamMapNet baseline on 60 m range and greater improvements on larger ranges. We confirm transferability by applying our method to another baseline, SQD-MapNet, and find similar improvements. A detailed analysis of the latent BEV grid confirms a more structured latent space of AugMapNet and shows the value of our novel concept beyond pure performance improvement. The code can be found at https://github.com/tmonnin/augmapnet
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