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

• URL: http://arxiv.org/abs/2608.01470v1
• Date: Sun, 02 Aug 2026 20:07:28 GMT
• Title: VGER: Voxel-Guided Global Event Ranking for Event Cloud Attribution
• Abstract summary: Voxel-Guided Global Event Ranking (VGER) is a training-free attribution framework for pointbased event cloud networks.<n>VGER combines event-level gradient evidence with task-aware temporalel evidence, transferring regional contribution into event-level attribution scores.<n>We evaluate VGER on three event-based benchmarks with PointNet, PointNet++ and EventMamba.
• Score: 16.49307024552495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras produce sparse and asynchronous event streams that provide rich spatio-temporal information for efficient perception. Recent advances in event-based models have demonstrated strong performance by directly modeling asynchronous events without dense frame reconstruction. However, identifying the event-level evidence behind their predictions is crucial for improving model transparency and reliability. Directly adapting point-level saliency methods from point clouds provides fine-grained attribution but overlooks event-specific spatio-temporal structures. To address this limitation, we propose Voxel-Guided Global Event Ranking (VGER), a training-free attribution framework for point-based event cloud networks. VGER combines event-level gradient evidence with task-aware voxel perturbation evidence, transferring regional contribution into event-level attribution scores while preserving fine-grained resolution. Furthermore, VGER introduces a unified event ranking strategy, where high-ranked events are expected to be prediction-critical and low-ranked events are expected to have limited influence on predictions. We evaluate VGER on three event-based benchmarks with PointNet, PointNet++, and EventMamba. Across nine dataset-backbone settings, VGER consistently improves both high-tail and low-tail deletion performance over point-level saliency baselines.

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