VGER: Voxel-Guided Global Event Ranking for Event Cloud Attribution

Event cameras produce sparse, asynchronous streams in which spatial coordinates and timestamps play fundamentally different physical and semantic roles. Consequently, saliency methods developed for static 3D point clouds do not transfer naturally to event point clouds. We introduce VGER, a post-hoc event-level attribution method for point-based event recognition models. VGER combines anisotropic gradient evidence, a task-aware voxel prior, and local rank calibration. Its anisotropic formulation models spatial, temporal, and joint spatiotemporal sensitivity separately instead of treating normalized spatial and temporal coordinates as geometrically interchangeable. Changes in task loss under voxel perturbations identify relevant spatiotemporal regions, while local spatial and temporal ranks distribute this regional evidence among individual events. The resulting global score requires no model retraining and supports a unified two-tail evaluation: removing highly ranked events should rapidly degrade recognition performance, whereas removing low-ranked events should preserve it. We evaluate VGER using a proportional-deletion protocol on DVS Gesture, N-MNIST, and N-Caltech101 with PointNet, PointNet++, and EventMamba backbones. The evaluation examines attribution fidelity, stability, robustness, cross-backbone consistency, and computational efficiency.