用虚拟相机精修3D高斯分割边界，提升细节精度
GaussianTrimmer: Online Trimming Boundaries for 3DGS Segmentation
- 通过生成均匀覆盖的虚拟相机视角，实现边界区域精细建模
- 在原始高斯基础上进行边界裁剪，显著改善分割轮廓质量
- 无需重训练，可直接用于现有3D高斯分割方法
随着3D高斯在3D场景表示中的广泛应用，基于3D高斯的3D场景分割方法也逐渐兴起。然而，现有方法多基于高斯原语进行分割，由于3D高斯尺度变化大，大尺寸高斯常同时覆盖前景与背景，导致分割边界锯齿状、不清晰。为此，本文提出一种在线边界裁剪方法GaussianTrimmer，该方法为高效且即插即用的后处理方案，可对现有3D高斯分割方法的粗边界进行优化。方法包含两个核心步骤：1. 生成均匀且充分覆盖的虚拟相机；2. 基于虚拟相机上的2D分割结果，在高斯原语层面进行边界裁剪。大量定量与定性实验表明，该方法作为即插即用模块，可有效提升现有3D高斯分割方法的分割质量。
原文摘要 · Abstract (English)
With the widespread application of 3D Gaussians in 3D scene representation, 3D scene segmentation methods based on 3D Gaussians have also gradually emerged. However, existing 3D Gaussian segmentation methods basically segment on the basis of Gaussian primitives. Due to the large variation range of the scale of 3D Gaussians, large-sized Gaussians that often span the foreground and background lead to jagged boundaries of segmented objects. To this end, we propose an online boundary trimming method, GaussianTrimmer, which is an efficient and plug-and-play post-processing method capable of trimming coarse boundaries for existing 3D Gaussian segmentation methods. Our method consists of two core steps: 1. Generating uniformly and well-covered virtual cameras; 2. Trimming Gaussian at the primitive level based on 2D segmentation results on virtual cameras. Extensive quantitative and qualitative experiments demonstrate that our method can improve the segmentation quality of existing 3D Gaussian segmentation methods as a plug-and-play method.
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