PRISM3D: Probabilistic Refinement and Robust Initialization for Physically Consistent Scene Modeling under Extreme Motion Blur

We address the inverse problem of blind 3D scene recon-struction from extremely motion-blurred images, a scenario where tradi-tional Structure-from-Motion (SfM) pipelines fail. Existing approachestypically circumvent this bottleneck by relying on impractical sharp-imagesupervision. In this work, we introduce PRISM3D, a unified frameworkenabling robust reconstruction directly from severely degraded inputs.To overcome the lack of a reliable starting point, we propose a RobustInitialization strategy utilizing deep dense tracking method (VGGSfM)to recover global topology where feature matching fails. To the best of ourknowledge, we are the first to effectively leverage this paradigm to boot-strap 3D Gaussian Splatting from extreme motion blur. However, whilerobust, this initialization yields sparse and noisy geometry that causes de-terministic optimization to diverge. To resolve this, we propose a coupledsolution driven by probability and physics: we adopt a probabilisticformulation for geometric densification via Markov Chain Monte Carlo(MCMC) to robustly populate the sparse priors, while simultaneouslymodeling physical image formation via continuous Bézier Trajectories.Furthermore, while PRISM3D establishes a highly robust standalonepipeline, the availability of complementary event streams offers an op-portunity to push the reconstruction fidelity further. To exploit this, weintroduce PRISM3D-E, a multi-modal (RGB + Events) extension thatseamlessly integrates high-temporal-resolution events as structural priorsto maximize geometric recovery. Because existing datasets lack pairedevent streams under such severe degradation, we concurrently contributethe PRISM3D-E Benchmark to facilitate rigorous evaluation. Exten-sive experiments demonstrate that both our standalone RGB frameworkand its multi-modal extension establish new state-of-the-art performance.To facilitate future research in extreme motion deblurring, our projectpage, featuring extensive qualitative comparisons, video results, and theupcoming releases of our codebase and benchmark dataset, is availableat https://gopirajumatta.github.io/PRISM3D/.