发表机构
NEC Corporation; Miraikan - The National Museum of Emerging Science and Innovation; Keio University; Carnegie Mellon University（日本电气株式会社; 日本科学未来馆; 庆应义塾大学; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出EgoNeMo框架，利用自我中心3D激光雷达点云和神经隐式建模，通过位置平衡采样与多任务学习，实现未知环境下行人动力学地图的可迁移重建，并提升下游轨迹预测可靠性。
AI 中文摘要
本文提出了一种可迁移的动力学地图（MoD）框架，该框架仅利用自我中心的三维激光雷达点云即可泛化到未知环境，从而克服了传统MoD方法长期存在的局限性。虽然MoD对于编码人体运动特征以实现准确的行人轨迹预测或安全的机器人导航至关重要，但传统方法受限于场地特异性，需要在每个新位置进行详尽的轨迹积累。我们扩展了神经隐式建模的最新进展，在不同环境中训练了一个连续的、基于激光雷达的MoD估计器。为缓解真实世界轨迹数据固有的稀疏性和时间偏差，我们引入了一种位置平衡采样策略和一种多任务学习架构，该架构联合预测运动分布和空间频率得分图。后者进一步通过可见性感知损失进行增强，以补偿不完整的观测数据。综合实验表明，尽管训练数据高度稀疏，我们的方法即使在未知位置也能从单次瞬时激光雷达扫描中有效重建底层运动图。最后，我们证明了我们的改进增强了下游轨迹预测的可靠性。
英文摘要
This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds to overcome the long-standing limitation of traditional MoD methods. While MoDs are essential for encoding human motion characteristics to enable accurate pedestrian trajectory prediction or safe robot navigation, traditional approaches suffer from site-specificity, requiring exhaustive trajectory accumulation at every new location. Extending recent advances in neural implicit modeling, our framework trains a continuous, LiDAR-based MoD estimator across diverse environments. To mitigate the inherent sparsity and temporal bias of real-world trajectory data, we introduce a position-balanced sampling strategy and a multi-task learning architecture that jointly predicts motion distributions and a spatial frequency score map. The latter is further augmented by visibility-aware losses to compensate for incomplete observation data. Comprehensive experiments demonstrate that our method effectively reconstructs underlying motion maps even in unknown locations from a single instantaneous LiDAR scan, despite highly sparse training data. Finally, we show that our improvements enhance the reliability of downstream trajectory prediction.
Comments16 pages, 9 figures
