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.
Gaussian, a recently proposed explicit representation method, has attained remarkable achievements in three-dimensional scene reconstruction . Its biggest advantage is the capability of real-time rendering. Utilizing a series of scene images and corresponding camera data, it employs 3D Gaussians to depict scene objects. Each 3D Gaussian is defined by parameters including mean, covariance matrix, opacity, and spherical harmonics. The mean pinpoints the Gaussian’s central position in the 3D scene.
adds semantic features to specific points and incorporates DINO features of rendered images into this framework through a teacher-student model, allowing for feature interpolation at any given point. Techniques such as K-means clustering, nearest neighbor matching, and bilateral search are integrated, enabling interactive NeRF segmentation.
Gaussian , a recently proposed explicit representation method, has attained remarkable achievements in three-dimensional scene reconstruction . Its biggest advantage is the capability of real-time rendering. Utilizing a series of scene images and corresponding camera data, it employs 3D Gaussians to depict scene objects. Each 3D Gaussian is defined by parameters including mean, covariance matrix, opacity, and spherical harmonics.
adds semantic features to specific points and incorporates DINO features of rendered images into this framework through a teacher-student model, allowing for feature interpolation at any given point. Techniques such as K-means clustering, nearest neighbor matching, and bilateral search are integrated, enabling interactive NeRF segmentation.
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