Exploration Matters for Escaping the Blur Trap in 3D Gaussian Splatting

2026-07-20

AI Summary

This work addresses the susceptibility of 3D Gaussian Splatting (3DGS) to gradient bias in non-convex optimization, which often leads to convergence to suboptimal local minima known as the “Blur Trap,” resulting in blurry renderings. The study presents the first systematic analysis of this phenomenon, categorizing Blur Trap into two distinct types: Far-Side and Near-Side. To mitigate these issues, the authors introduce corresponding explicit exploration mechanisms—Random Seeding and Random Splitting—integrated directly into the 3DGS optimization pipeline to enhance global search capability. Experimental results across multiple datasets demonstrate that the proposed approach significantly improves reconstruction quality, effectively and complementarily alleviating the Blur Trap problem.

Abstract

3D Gaussian Splatting (3DGS) employs Gaussian primitives for explicit scene representation, facilitating real-time, high-fidelity reconstruction and novel view synthesis of complex scenes. However, the explicit modeling inherent in 3DGS introduces a gradient bias during optimization, rendering its non-convex optimization process highly susceptible to convergence toward local suboptimal solutions. This constitutes a fundamental limitation in 3DGS optimization, which we term the Blur Trap. To address this limitation, we integrate simple explicit exploration into the 3DGS optimization framework. First, through rigorous mathematical analysis of the 3DGS optimization formulation, we identify the underlying optimization bias responsible for the Blur Trap and categorize it into two distinct subtypes: the Far-Side Blur Trap and the Near-Side Blur Trap. Subsequently, we propose two highly straightforward exploration strategies (Random Seeding and Random Splitting) to mitigate the far-side and near-side blur traps, respectively. Experimental validation demonstrates that the incorporation of these exploration operators effectively and complementarily overcome the Blur Trap, achieving high-quality rendering performance across multiple datasets. Project page: https://chengbo-wang.github.io/ExploreGS/

Problem

• 3D Gaussian Splatting
• Blur Trap
• optimization bias
• local optima
• non-convex optimization

Innovation

• Blur Trap
• 3D Gaussian Splatting
• Exploration Strategy
• Random Seeding
• Random Splitting