发表机构
University of Washington; Rutgers University（华盛顿大学; 罗格斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在从航拍图像生成行人路径，核心方法是用TraversRL视觉条件模型，通过特定动作空间和奖励机制迭代生长路径网络，主要贡献是相比基线提升交并比与连通性指标，结合奖励生成更优网络，证明该建模方法的有效性。
AI 中文摘要
从航拍图像自动生成行人路径需要构建适用于路线规划的连通网络，而非仅检测人行道位置。现有基于分割的方法常生成不可靠的导航图。我们引入TraversRL，一个视觉条件模型，从航拍图像迭代生长路径网络。它使用长短方向距离段的动作空间，结合图级和逐步奖励。在三个视觉骨干网络和三个交叉数据集上，TraversRL相对于分割基线大幅提高了缓冲交并比，连通性指标翻倍。结合全局和局部奖励能产生更优网络。结果表明将路径提取建模为旅行者视角的序列决策过程并用强化学习优化最终图质量，能生成更可靠的行人网络。
英文摘要
Automatically generating pedestrian pathways from aerial images requires producing a connected network suitable for routing, not just detecting where sidewalks appear. Sidewalks and crossings, in contrast to roads, may be partially occluded, implicitly defined, and exhibit complex connectivity patterns. Existing segmentation-based approaches focus on labeling pixels to infer segments, but often produce disconnected or fragmentary graphs that are unreliable for navigation. We introduce TraversRL, a vision-conditioned model that iteratively grows a pathway network from an aerial image, simulating a traveler navigating the built environment. TraversRL uses an action space of short and long direction-distance segments designed to adapt to complex patterns and span occlusions, and uses a combination of graph-level and step-wise rewards to balance overall connectivity with precise edge placement. Across three visual backbones and three intersection datasets, TraversRL substantially improves buffered IoU with the ground-truth graph relative to a state-of-the-art segmentation baseline, and more than doubles metrics of connectivity. Moreover, combining global and local rewards produces cleaner graphs with fewer spurious branches while further improving overall performance. These results demonstrate that modeling pathway extraction as a sequential decision process from the perspective of a traveler, while optimizing for final graph quality with reinforcement learning, produces significantly more reliable pedestrian networks.
CommentsAccepted to ECCV 2026, main conference
