Clustered Error Correction with Grouped 4D Gaussian Splatting

2025-11-20

AI Summary

Existing 4D Gaussian Splatting (4DGS) methods suffer from ambiguous pixel correspondences and insufficient point density in dynamic regions, leading to temporal inconsistency and rendering artifacts in dynamic scene reconstruction. This paper proposes a grouped 4D Gaussian rendering framework addressing these issues. Its core contributions are: (1) a dynamic error modeling and backward-projection correction mechanism based on elliptical reprojection error clustering; (2) a mapping consistency constraint integrating foreground segmentation with cross-view color consistency analysis; and (3) an adaptive point cloud update strategy enabling erroneous point localization and dynamic initialization of new Gaussians. Evaluated on the Neural 3D Video and Technicolor light-field datasets, our method achieves state-of-the-art performance, improving PSNR by 0.39 dB over prior work, while significantly enhancing object alignment accuracy and temporal stability.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Existing 4D Gaussian Splatting (4DGS) methods struggle to accurately reconstruct dynamic scenes, often failing to resolve ambiguous pixel correspondences and inadequate densification in dynamic regions. We address these issues by introducing a novel method composed of two key components: (1) Elliptical Error Clustering and Error Correcting Splat Addition that pinpoints dynamic areas to improve and initialize fitting splats, and (2) Grouped 4D Gaussian Splatting that improves consistency of mapping between splats and represented dynamic objects. Specifically, we classify rendering errors into missing-color and occlusion types, then apply targeted corrections via backprojection or foreground splitting guided by cross-view color consistency. Evaluations on Neural 3D Video and Technicolor datasets demonstrate that our approach significantly improves temporal consistency and achieves state-of-the-art perceptual rendering quality, improving 0.39dB of PSNR on the Technicolor Light Field dataset. Our visualization shows improved alignment between splats and dynamic objects, and the error correction method's capability to identify errors and properly initialize new splats. Our implementation details and source code are available at https://github.com/tho-kn/cem-4dgs.

Problem

• Resolving ambiguous pixel correspondences in dynamic scene reconstruction
• Addressing inadequate densification in dynamic regions of 4DGS methods
• Improving temporal consistency and perceptual rendering quality

Innovation

• Elliptical Error Clustering for dynamic area identification
• Grouped 4D Gaussian Splatting for object consistency
• Targeted error correction via cross-view consistency