Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing complex scenes and training duration. These advantages signal a wide range of applications for 3D Gaussians in 3D understanding and editing. Meanwhile, the segmentation of 3D Gaussians is still in its infancy. The existing segmentation methods are not only cumbersome but also incapable of segmenting multiple objects simultaneously in a short amount of time. In response, this paper introduces a 3D Gaussian segmentation method implemented with 2D segmentation as supervision. This approach uses input 2D segmentation maps to guide the learning of the added 3D Gaussian semantic information, while nearest neighbor clustering and statistical filtering refine the segmentation results. Experiments show that our concise method can achieve comparable performances on mIOU and mAcc for multi-object segmentation as previous single-object segmentation methods.

翻译：近期，3D高斯作为一种显式三维表示方法，在复杂场景表达与训练效率方面展现出相较于NeRF（神经辐射场）的显著优势。这些优势预示着3D高斯在三维理解与编辑领域的广阔应用前景。然而，3D高斯的语义分割技术仍处于探索初期。现有分割方法不仅流程繁琐，且无法在短时间内实现多目标同步分割。为此，本文提出一种以2D分割作为监督信号的3D高斯分割方法。该方法通过输入二维分割图引导新增的3D高斯语义信息学习，同时结合最近邻聚类与统计滤波优化分割结果。实验表明，这种简洁方法在多目标分割的mIOU与mAcc指标上可达到与以往单目标分割方法相媲美的性能。