让3D高斯点均匀分布并贴合表面,提升重建精度与编辑性。
Gaussian Set Surface Reconstruction through Per-Gaussian Optimization
- 通过像素级与高斯级法向一致性,实现局部精细对齐。
- 优化后高斯点分布更均匀,几何误差显著降低。
- 适合需要精准重建和交互式编辑的3D场景应用。
3D高斯泼溅(3DGS)虽能高效生成新视角,但几何重建精度不足。现有方法如PGSR虽引入深度与法向损失,仍忽略单个高斯点的位置优化,导致点云分布不均且偏离潜在表面,影响后续优化与编辑。受点集曲面研究启发,本文提出高斯集表面重建(GSSR),通过像素级与高斯级单视图法向一致性及多视图光度一致性,实现局部与全局几何对齐,使高斯点均匀分布于潜在表面并匹配表面法向。进一步引入不透明度正则化损失以剔除冗余点,并结合深度与法向引导的周期性重初始化,提升空间分布均匀性。实验表明,该方法在保持高质量渲染的同时,显著提升几何精度,支持直观场景编辑与高效3D环境生成。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) effectively synthesizes novel views through its flexible representation, yet fails to accurately reconstruct scene geometry. While modern variants like PGSR introduce additional losses to ensure proper depth and normal maps through Gaussian fusion, they still neglect individual placement optimization. This results in unevenly distributed Gaussians that deviate from the latent surface, complicating both reconstruction refinement and scene editing. Motivated by pioneering work on Point Set Surfaces, we propose Gaussian Set Surface Reconstruction (GSSR), a method designed to distribute Gaussians evenly along the latent surface while aligning their dominant normals with the surface normal. GSSR enforces fine-grained geometric alignment through a combination of pixel-level and Gaussian-level single-view normal consistency and multi-view photometric consistency, optimizing both local and global perspectives. To further refine the representation, we introduce an opacity regularization loss to eliminate redundant Gaussians and apply periodic depth- and normal-guided Gaussian reinitialization for a cleaner, more uniform spatial distribution. Our reconstruction results demonstrate significantly improved geometric precision in Gaussian placement, enabling intuitive scene editing and efficient generation of novel Gaussian-based 3D environments. Extensive experiments validate GSSR's effectiveness, showing enhanced geometric accuracy while preserving high-quality rendering performance.
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