提出两种探索策略，解决3D高斯点云渲染中的模糊陷阱问题
Exploration Matters for Escaping the Blur Trap in 3D Gaussian Splatting
- 通过数学分析识别出远侧与近侧模糊陷阱
- 引入随机初始化与分裂策略有效克服模糊陷阱
- 在多个数据集上实现高质量重建，适合3D重建研究者
3D高斯点云（3DGS）使用高斯原语显式表示场景，支持复杂场景的实时、高保真重建与新视角合成。然而，3DGS的显式建模在优化过程中引入梯度偏差，导致非凸优化极易陷入局部次优解，这一根本性局限被称为模糊陷阱。本文通过严谨的数学分析，揭示了造成模糊陷阱的优化偏差，并将其分为远侧模糊陷阱与近侧模糊陷阱两类。为此，提出两种简单有效的探索策略：随机初始化用于缓解远侧模糊陷阱，随机分裂用于应对近侧模糊陷阱。实验验证表明，这些探索操作能有效且互补地克服模糊陷阱，在多个数据集上实现高质量渲染效果。项目主页：https://chengbo-wang.github.io/ExploreGS/
原文摘要 · Abstract (English)
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/
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