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[2510.12768] Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction
Summary
This paper presents USplat4D, a novel framework for monocular 4D reconstruction that incorporates uncertainty in dynamic Gaussian splatting, enhancing stability and synthesis quality in complex scenes.
Why It Matters
Understanding uncertainty in dynamic environments is crucial for improving the accuracy of 3D reconstructions from monocular inputs. This research addresses significant challenges in computer vision, particularly in scenarios involving occlusion and varying visibility, which are common in real-world applications.
Key Takeaways
- Dynamic Gaussian Splatting can be improved by incorporating uncertainty.
- USplat4D framework enhances the reliability of motion cues in 4D reconstruction.
- Explicit modeling of uncertainty leads to better performance under occlusion.
- The approach yields higher quality synthesis at extreme viewpoints.
- Experiments show consistent improvements across diverse datasets.
Computer Science > Computer Vision and Pattern Recognition arXiv:2510.12768 (cs) [Submitted on 14 Oct 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction Authors:Fengzhi Guo, Chih-Chuan Hsu, Sihao Ding, Cheng Zhang View a PDF of the paper titled Uncertainty Matters in Dynamic Gaussian Splatting for Monocular 4D Reconstruction, by Fengzhi Guo and Chih-Chuan Hsu and Sihao Ding and Cheng Zhang View PDF HTML (experimental) Abstract:Reconstructing dynamic 3D scenes from monocular input is fundamentally under-constrained, with ambiguities arising from occlusion and extreme novel views. While dynamic Gaussian Splatting offers an efficient representation, vanilla models optimize all Gaussian primitives uniformly, ignoring whether they are well or poorly observed. This limitation leads to motion drifts under occlusion and degraded synthesis when extrapolating to unseen views. We argue that uncertainty matters: Gaussians with recurring observations across views and time act as reliable anchors to guide motion, whereas those with limited visibility are treated as less reliable. To this end, we introduce USplat4D, a novel Uncertainty-aware dynamic Gaussian Splatting framework that propagates reliable motion cues to enhance 4D reconstruction. Our approach estimates time-varying per-Gaussian uncertainty and leverages it to construct a spatio-temporal graph for uncertainty-aware optimization. Expe...
