EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan

• URL: http://arxiv.org/abs/2609.06195v1
• Date: Sat, 05 Sep 2026 17:35:21 GMT
• Title: EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan
• Abstract summary: This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments.<n>MoDs are essential for encoding human motion characteristics to enable accurate pedestrian trajectory prediction or safe robot navigation.<n>Our framework trains a continuous, LiDAR-based MoD estimator across diverse environments.
• Score: 13.267842001048502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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