通过追踪高斯实例，实现多视角一致的2D到3D分割提升
Trace3D: Consistent Segmentation Lifting via Gaussian Instance Tracing
- 用实例权重矩阵增强高斯表示，自动修正2D分割不一致
- 自适应密度控制机制使高斯点更清晰，边界更锐利
- 适合需要精准3D物体分割的场景编辑与层级分割任务
针对高斯点云中从2D视觉分割到3D的提升问题，现有方法常因多视角间2D掩码不一致且忽略语义信息而产生噪声边界。本文提出高斯实例追踪（GIT），在标准高斯表示中引入跨视图的实例权重矩阵，利用3D高斯的一致性识别并纠正2D分割不一致。由于每个高斯理想对应单一物体，我们进一步设计了基于GIT的自适应密度控制机制，在训练中分裂和修剪模糊高斯，获得更清晰、连贯的2D与3D分割边界。实验表明，该方法在在线（如自提示）和离线（如对比提升）设置下均显著改善3D分割质量，可支持层次化分割、物体提取与场景编辑等应用。
原文摘要 · Abstract (English)
We address the challenge of lifting 2D visual segmentation to 3D in Gaussian Splatting. Existing methods often suffer from inconsistent 2D masks across viewpoints and produce noisy segmentation boundaries as they neglect these semantic cues to refine the learned Gaussians. To overcome this, we introduce Gaussian Instance Tracing (GIT), which augments the standard Gaussian representation with an instance weight matrix across input views. Leveraging the inherent consistency of Gaussians in 3D, we use this matrix to identify and correct 2D segmentation inconsistencies. Furthermore, since each Gaussian ideally corresponds to a single object, we propose a GIT-guided adaptive density control mechanism to split and prune ambiguous Gaussians during training, resulting in sharper and more coherent 2D and 3D segmentation boundaries. Experimental results show that our method extracts clean 3D assets and consistently improves 3D segmentation in both online (e.g., self-prompting) and offline (e.g., contrastive lifting) settings, enabling applications such as hierarchical segmentation, object extraction, and scene editing.
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