title: FlashSplat: 2D to 3D Gaussian Splatting Segmentation Solved Optimally
summary: This study addresses the challenge of accurately segmenting 3D Gaussian
  Splatting from 2D masks. Conventional methods often rely on iterative gradient
  descent to assign each Gaussian a unique label, leading to lengthy optimization
  and sub-optimal solutions. Instead, we propose a straightforward yet globally
  optimal solver for 3D-GS segmentation. The core insight of our method is that,
  with a reconstructed 3D-GS scene, the rendering of the 2D masks is essentially
  a linear function with respect to the labels of each Gaussian. As such, the
  optimal label assignment can be solved via linear programming in closed form.
  This solution capitalizes on the alpha blending characteristic of the splatting
  process for single step optimization. By incorporating the background bias in
  our objective function, our method shows superior robustness in 3D segmentation
  against noises. Remarkably, our optimization completes within 30 seconds, about
  50times faster than the best existing methods. Extensive experiments
  demonstrate the efficiency and robustness of our method in segmenting various
  scenes, and its superior performance in downstream tasks such as object removal
  and inpainting. Demos and code will be available at
  https://github.com/florinshen/FlashSplat.
paper_authors._id: 66e39c30f5579b829fe5e4f1
paper_authors.name: Qiuhong Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-13T08:05:33.263Z
paper_authors._id: 643a6e89a856622f9788bf67
paper_authors.avatarUrl: /avatars/419c0379f072295b27d4bfe2f8fb946d.svg
paper_authors.fullname: qiuhong shen
paper_authors.type: user
paper_authors.user: florinshum
paper_authors._id: 66e39c30f5579b829fe5e4f2
paper_authors.name: Xingyi Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-13T08:05:22.780Z
paper_authors._id: 634cfebc350bcee9bed20a4d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634cfebc350bcee9bed20a4d/fN47nN5rhw-HJaFLBZWQy.png
paper_authors.fullname: Xingyi Yang
paper_authors.type: user
paper_authors.user: adamdad
paper_authors._id: 66e39c30f5579b829fe5e4f3
paper_authors.name: Xinchao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-13T08:06:21.872Z
paper_authors._id: 63fc03a50aab060792ffef39
paper_authors.avatarUrl: /avatars/9d5b1bb2a41928e08176b703935133ab.svg
paper_authors.fullname: Wangxinchao
paper_authors.type: user
paper_authors.user: wxcTest
paper_title: FlashSplat: 2D to 3D Gaussian Splatting Segmentation Solved Optimally
paper_summary: This study addresses the challenge of accurately segmenting 3D Gaussian
  Splatting from 2D masks. Conventional methods often rely on iterative gradient
  descent to assign each Gaussian a unique label, leading to lengthy optimization
  and sub-optimal solutions. Instead, we propose a straightforward yet globally
  optimal solver for 3D-GS segmentation. The core insight of our method is that,
  with a reconstructed 3D-GS scene, the rendering of the 2D masks is essentially
  a linear function with respect to the labels of each Gaussian. As such, the
  optimal label assignment can be solved via linear programming in closed form.
  This solution capitalizes on the alpha blending characteristic of the splatting
  process for single step optimization. By incorporating the background bias in
  our objective function, our method shows superior robustness in 3D segmentation
  against noises. Remarkably, our optimization completes within 30 seconds, about
  50times faster than the best existing methods. Extensive experiments
  demonstrate the efficiency and robustness of our method in segmenting various
  scenes, and its superior performance in downstream tasks such as object removal
  and inpainting. Demos and code will be available at
  https://github.com/florinshen/FlashSplat.
paper_ai_keywords: 3D Gaussian Splatting 3D-GS segmentation gradient descent linear programming alpha blending