用分割驱动初始化，让3D高斯点云更精简高效
Segmentation-Driven Initialization for Sparse-view 3D Gaussian Splatting
- 通过区域分割筛选重要结构，只保留关键区域的高斯点
- 高斯数量减少50%，渲染质量在PSNR/SSIM上持平甚至超越
- 适合低视角数、内存受限的3D重建场景
稀疏视角合成因观测有限而难以恢复准确几何与外观。尽管3D高斯喷溅（3DGS）已实现实时渲染且质量优异，现有方法多依赖结构从运动（SfM）估计相机位姿，这在真正稀疏视角下表现不佳。部分无SfM方法改用多视角立体（MVS）模型，但会将每个像素反投影至3D空间，生成海量3D高斯点，导致内存开销巨大。本文提出分割驱动的高斯喷溅初始化（SDI-GS），利用基于区域的分割识别并保留结构显著区域，实现对密集点云的有选择性下采样，在保持场景保真度的同时大幅降低高斯点数量。跨多种基准测试表明，SDI-GS可将高斯点数量减少最多50%，在PSNR和SSIM指标上达到或优于基线，仅在LPIPS上有轻微下降。该方法还支持更快训练与更低内存占用，提升了3DGS在受限视角场景下的实用性。
原文摘要 · Abstract (English)
Sparse-view synthesis remains a challenging problem due to the difficulty of recovering accurate geometry and appearance from limited observations. While recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time rendering with competitive quality, existing pipelines often rely on Structure-from-Motion (SfM) for camera pose estimation, an approach that struggles in genuinely sparse-view settings. Moreover, several SfM-free methods replace SfM with multi-view stereo (MVS) models, but generate massive numbers of 3D Gaussians by back-projecting every pixel into 3D space, leading to high memory costs. We propose Segmentation-Driven Initialization for Gaussian Splatting (SDI-GS), a method that mitigates inefficiency by leveraging region-based segmentation to identify and retain only structurally significant regions. This enables selective downsampling of the dense point cloud, preserving scene fidelity while substantially reducing Gaussian count. Experiments across diverse benchmarks show that SDI-GS reduces Gaussian count by up to 50% and achieves comparable or superior rendering quality in PSNR and SSIM, with only marginal degradation in LPIPS. It further enables faster training and lower memory footprint, advancing the practicality of 3DGS for constrained-view scenarios.
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