构建城市人行道视觉导航基准，评估模型在复杂环境中的表现。
SidewalkBench: Benchmarking Visual Navigation on Urban Sidewalks
- 基于NVIDIA Isaac Sim构建高保真仿真环境，支持程序生成与真实扫描场景。
- 在330个单元测试、800个行人交互、105个长程任务中评估9个模型性能。
- 揭示行人交互与长程规划是当前模型主要瓶颈，合成数据训练有效提升表现。
城市人行道导航因复杂的结构布局、动态行人行为和长距离路径而面临巨大挑战。尽管近期视觉导航模型展现出潜力，但缺乏统一基准导致定量与可复现评估困难。为此，我们提出Sidewench，一个面向城市人行道视觉导航的综合性基准。该基准基于NVIDIA Isaac Sim，实现多样化、高保真人行道环境的GPU加速仿真，涵盖程序生成与真实扫描场景。我们进一步在场景中注入丰富且响应式的事件驱动行人行为，结合灵活高效的动画机制，实现真实世界条件下的标准化模型评估。我们在330个单元测试场景、800个行人交互场景和105个长程任务中对9个视觉导航模型进行了全面评估。结果表明，行人交互与长程鲁棒性仍是现有模型的关键瓶颈，通过合成数据扩展人行道训练成为有前景的解决方案。
原文摘要 · Abstract (English)
Urban sidewalk navigation presents significant challenges due to complex structural layouts, dynamic pedestrian behaviors, and long distances. While recent visual navigation models offer a promising solution, the lack of a unified benchmark hinders quantitative and reproducible evaluation. To bridge this gap, we propose SidewalkBench, a comprehensive benchmark designed for visual navigation on urban sidewalks. Built upon NVIDIA Isaac Sim, SidewalkBench brings GPU-accelerated simulation of diverse, high-fidelity sidewalk environments, including both procedurally generated and real-world scanned scenes. We further populate the scenes with rich, reactive event-based pedestrian behaviors and flexible, efficient animation, enabling standardized model evaluation under realistic real-world settings. We conduct a comprehensive evaluation of 9 visual navigation models on 330 unit-test scenarios, 800 pedestrian-reactive scenarios, and 105 long-horizon scenarios. Our findings highlight that pedestrian interaction and long-horizon robustness remain critical bottlenecks for existing models, and scaling up sidewalk training with synthetic data emerges as a promising solution.
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