用栅格多边形重构道路元素，提升高精地图生成泛化能力
Polymap: generating high definition map based on rasterized polygons
- 将道路要素视为栅格多边形，通过分割式Transformer端到端生成实例掩码
- 在Nuscenes数据集上实现更优的泛化性能，优于检测类方法
- 适合需要高鲁棒性自动标注的自动驾驶地图构建场景
高精地图感知是自动驾驶环境感知的关键组成部分。现有研究多聚焦于在线构建高精地图，如Maptr系列采用基于检测的方法，以端到端方式并行输出矢量化地图实例。然而，尽管具备实时构建能力，检测类方法被发现泛化能力不足，限制了其在自动标注系统中的应用。为此，本文将道路元素重新定义为栅格多边形，设计一种基于实例分割的简洁框架。首先使用分割式Transformer端到端生成实例掩码；随后通过Potrace后处理模块，最终输出矢量化地图元素。在Nuscenes数据集上的定量结果验证了该方法的有效性与泛化能力。
原文摘要 · Abstract (English)
The perception of high-definition maps is an integral component of environmental perception in autonomous driving systems. Existing research have often focused on online construction of high-definition maps. For instance, the Maptr[9] series employ a detection-based method to output vectorized map instances parallelly in an end-to-end manner. However, despite their capability for real-time construction, detection-based methods are observed to lack robust generalizability[19], which hampers their applicability in auto-labeling systems. Therefore, aiming to improve the generalizability, we reinterpret road elements as rasterized polygons and design a concise framework based on instance segmentation. Initially, a segmentation-based transformer is employed to deliver instance masks in an end-to-end manner; succeeding this step, a Potrace-based[17] post-processing module is used to ultimately yield vectorized map elements. Quantitative results attained on the Nuscene[1] dataset substantiate the effectiveness and generaliz-ability of our method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。