title: Click-Gaussian: Interactive Segmentation to Any 3D Gaussians
summary: Interactive segmentation of 3D Gaussians opens a great opportunity for
  real-time manipulation of 3D scenes thanks to the real-time rendering
  capability of 3D Gaussian Splatting. However, the current methods suffer from
  time-consuming post-processing to deal with noisy segmentation output. Also,
  they struggle to provide detailed segmentation, which is important for
  fine-grained manipulation of 3D scenes. In this study, we propose
  Click-Gaussian, which learns distinguishable feature fields of two-level
  granularity, facilitating segmentation without time-consuming post-processing.
  We delve into challenges stemming from inconsistently learned feature fields
  resulting from 2D segmentation obtained independently from a 3D scene. 3D
  segmentation accuracy deteriorates when 2D segmentation results across the
  views, primary cues for 3D segmentation, are in conflict. To overcome these
  issues, we propose Global Feature-guided Learning (GFL). GFL constructs the
  clusters of global feature candidates from noisy 2D segments across the views,
  which smooths out noises when training the features of 3D Gaussians. Our method
  runs in 10 ms per click, 15 to 130 times as fast as the previous methods, while
  also significantly improving segmentation accuracy. Our project page is
  available at https://seokhunchoi.github.io/Click-Gaussian
paper_authors._id: 66973b46c1d9adc0c7d6b0d1
paper_authors.name: Seokhun Choi
paper_authors._id: 66973b46c1d9adc0c7d6b0d2
paper_authors.name: Hyeonseop Song
paper_authors._id: 66973b46c1d9adc0c7d6b0d3
paper_authors.name: Jaechul Kim
paper_authors._id: 66973b46c1d9adc0c7d6b0d4
paper_authors.name: Taehyeong Kim
paper_authors._id: 66973b46c1d9adc0c7d6b0d5
paper_authors.name: Hoseok Do
paper_title: Click-Gaussian: Interactive Segmentation to Any 3D Gaussians
paper_summary: Interactive segmentation of 3D Gaussians opens a great opportunity for
  real-time manipulation of 3D scenes thanks to the real-time rendering
  capability of 3D Gaussian Splatting. However, the current methods suffer from
  time-consuming post-processing to deal with noisy segmentation output. Also,
  they struggle to provide detailed segmentation, which is important for
  fine-grained manipulation of 3D scenes. In this study, we propose
  Click-Gaussian, which learns distinguishable feature fields of two-level
  granularity, facilitating segmentation without time-consuming post-processing.
  We delve into challenges stemming from inconsistently learned feature fields
  resulting from 2D segmentation obtained independently from a 3D scene. 3D
  segmentation accuracy deteriorates when 2D segmentation results across the
  views, primary cues for 3D segmentation, are in conflict. To overcome these
  issues, we propose Global Feature-guided Learning (GFL). GFL constructs the
  clusters of global feature candidates from noisy 2D segments across the views,
  which smooths out noises when training the features of 3D Gaussians. Our method
  runs in 10 ms per click, 15 to 130 times as fast as the previous methods, while
  also significantly improving segmentation accuracy. Our project page is
  available at https://seokhunchoi.github.io/Click-Gaussian
paper_ai_keywords: Click-Gaussian feature fields two-level granularity 3D Gaussian Splatting real-time rendering 2D segmentation 3D segmentation Global Feature-guided Learning (GFL) global feature candidates smoothing out noises 3D Gaussians