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.
The recently emerged 3D Gaussian technique marks a significant advancement over previous 3D representation methods such as point clouds , meshes , signed distance functions , and neural radiance fields , especially in terms of training time and scene reconstruction quality. The mean of each 3D Gaussian represents the position of its center point, the covariance matrix indicates rotation and size, and spherical harmonics express color.
is complex in its implementation and struggles with segmenting multiple objects simultaneously. Additionally, the explicit expression of 3D Gaussians leads to storage overhead, preventing it from directly transferring 2D semantic features into 3D, as in NeRF segmentation . Finally, the scarcity of datasets and the lack of annotations impede the application of supervised segmentation methods, commonly utilized in 2D and point cloud segmentation.
The recently emerged 3D Gaussian technique marks a significant advancement over previous 3D representation methods such as point clouds , meshes , signed distance functions , and neural radiance fields , especially in terms of training time and scene reconstruction quality. The mean of each 3D Gaussian represents the position of its center point, the covariance matrix indicates rotation and size, and spherical harmonics express color.
is complex in its implementation and struggles with segmenting multiple objects simultaneously. Additionally, the explicit expression of 3D Gaussians leads to storage overhead, preventing it from directly transferring 2D semantic features into 3D, as in NeRF segmentation . Finally, the scarcity of datasets and the lack of annotations impede the application of supervised segmentation methods, commonly utilized in 2D and point cloud segmentation.
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