2D-Guided 3D Gaussian Segmentation
Kun Lan Haoran Li Haolin Shi Wenjun Wu Yong Liao Lin Wang Pengyuan Zhou
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.