PEGS: Physics-Event Enhanced Large Spatiotemporal Motion Reconstruction via 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2511.17116v1
• Date: Fri, 21 Nov 2025 10:27:51 GMT
• Title: PEGS: Physics-Event Enhanced Large Spatiotemporal Motion Reconstruction via 3D Gaussian Splatting
• Authors: Yijun Xu, Jingrui Zhang, Hongyi Liu, Yuhan Chen, Yuanyang Wang, Qingyao Guo, Dingwen Wang, Lei Yu, Chu He,
• Abstract summary: PEGS is a framework that integrates Physical priors with Event stream enhancement within a 3D Gaussian Splatting pipeline.<n>We introduce a triple-level supervision scheme that enforces physical plausibility via an acceleration constraint.<n>We also contribute the first RGB-Event paired targeting the natural, fast rigid motion across diverse datasets.
• Score: 8.672740691555736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstruction of rigid motion over large spatiotemporal scales remains a challenging task due to limitations in modeling paradigms, severe motion blur, and insufficient physical consistency. In this work, we propose PEGS, a framework that integrates Physical priors with Event stream enhancement within a 3D Gaussian Splatting pipeline to perform deblurred target-focused modeling and motion recovery. We introduce a cohesive triple-level supervision scheme that enforces physical plausibility via an acceleration constraint, leverages event streams for high-temporal resolution guidance, and employs a Kalman regularizer to fuse multi-source observations. Furthermore, we design a motion-aware simulated annealing strategy that adaptively schedules the training process based on real-time kinematic states. We also contribute the first RGB-Event paired dataset targeting natural, fast rigid motion across diverse scenarios. Experiments show PEGS's superior performance in reconstructing motion over large spatiotemporal scales compared to mainstream dynamic methods.

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