Efficient 3D Gaussian segmentation guided by 2D models achieves fast, accurate multi-object segmentation, advancing 3D scene understanding and editing.
Authors: Kun Lan, University of Science and Technology of China; Haoran Li, University of Science and Technology of China; Haolin Shi, University of Science and Technology of China; Wenjun Wu, University of Science and Technology of China; Yong Liao, University of Science and Technology of China; Lin Wang, AI Thrust, HKUST; Pengyuan Zhou, University of Science and Technology of China. Table of Links Abstract and 1. Introduction 2. Related Work 3. Method and 3.1.
Given a well-trained scene using 3D Gaussian representation, scene rendering images, and corresponding camera parameters, we initially employed an interactive 2D segmentation model to segment the rendered images. Then, the obtained 2D segmentation maps are used as guidance to facilitate the learning of semantic information added to the 3D Gaussians.
Given a well-trained scene using 3D Gaussian representation, scene rendering images, and corresponding camera parameters, we initially employed an interactive 2D segmentation model to segment the rendered images. Then, the obtained 2D segmentation maps are used as guidance to facilitate the learning of semantic information added to the 3D Gaussians.
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