无需参考视图的稀疏视角3D高斯点云初始化方法
Pi-GS: Sparse-View Gaussian Splatting with Dense π^3 Initialization
- 用π^3网络无参考地生成密集点云作为初始化
- 在多个数据集上达到当前最优的新视角合成效果
- 适合相机位姿不准确或视图稀疏的重建场景
新视角合成技术已从神经辐射场发展到3D高斯喷溅（3DGS），实现了实时渲染与快速训练且保持视觉保真度。然而，3DGS高度依赖精确的相机位姿和高质量的点云初始化，在稀疏视角场景下难以获取。传统结构光重建（SfM）在此类设置中常失效，现有基于学习的点估计方法通常需可靠参考视图，且对位姿或深度误差敏感。本文提出一种鲁棒方法，利用π^3——一个无需参考的点云估计网络。将π^3生成的密集初始化与正则化方案结合，设计了不确定性引导的深度监督、法向一致性损失和深度扭曲机制，以缓解几何误差。实验表明，该方法在Tanks and Temples、LLFF、DTU和MipNeRF360数据集上均达到当前最优性能。
原文摘要 · Abstract (English)
Novel view synthesis has evolved rapidly, advancing from Neural Radiance Fields to 3D Gaussian Splatting (3DGS), which offers real-time rendering and rapid training without compromising visual fidelity. However, 3DGS relies heavily on accurate camera poses and high-quality point cloud initialization, which are difficult to obtain in sparse-view scenarios. While traditional Structure from Motion (SfM) pipelines often fail in these settings, existing learning-based point estimation alternatives typically require reliable reference views and remain sensitive to pose or depth errors. In this work, we propose a robust method utilizing π^3, a reference-free point cloud estimation network. We integrate dense initialization from π^3 with a regularization scheme designed to mitigate geometric inaccuracies. Specifically, we employ uncertainty-guided depth supervision, normal consistency loss, and depth warping. Experimental results demonstrate that our approach achieves state-of-the-art performance on the Tanks and Temples, LLFF, DTU, and MipNeRF360 datasets.
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