title: Towards Realistic Example-based Modeling via 3D Gaussian Stitching
summary: Using parts of existing models to rebuild new models, commonly termed as
  example-based modeling, is a classical methodology in the realm of computer
  graphics. Previous works mostly focus on shape composition, making them very
  hard to use for realistic composition of 3D objects captured from real-world
  scenes. This leads to combining multiple NeRFs into a single 3D scene to
  achieve seamless appearance blending. However, the current SeamlessNeRF method
  struggles to achieve interactive editing and harmonious stitching for
  real-world scenes due to its gradient-based strategy and grid-based
  representation. To this end, we present an example-based modeling method that
  combines multiple Gaussian fields in a point-based representation using
  sample-guided synthesis. Specifically, as for composition, we create a GUI to
  segment and transform multiple fields in real time, easily obtaining a
  semantically meaningful composition of models represented by 3D Gaussian
  Splatting (3DGS). For texture blending, due to the discrete and irregular
  nature of 3DGS, straightforwardly applying gradient propagation as SeamlssNeRF
  is not supported. Thus, a novel sampling-based cloning method is proposed to
  harmonize the blending while preserving the original rich texture and content.
  Our workflow consists of three steps: 1) real-time segmentation and
  transformation of a Gaussian model using a well-tailored GUI, 2) KNN analysis
  to identify boundary points in the intersecting area between the source and
  target models, and 3) two-phase optimization of the target model using
  sampling-based cloning and gradient constraints. Extensive experimental results
  validate that our approach significantly outperforms previous works in terms of
  realistic synthesis, demonstrating its practicality. More demos are available
  at https://ingra14m.github.io/gs_stitching_website.
paper_authors._id: 66cfdb63be74f57bd49d39bd
paper_authors.name: Xinyu Gao
paper_authors._id: 66cfdb63be74f57bd49d39be
paper_authors.name: Ziyi Yang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-08-29T02:22:32.743Z
paper_authors._id: 65d6b7f9e77bb42e9a652c7f
paper_authors.avatarUrl: /avatars/defa84e5df00a6599c217daffbcd4e08.svg
paper_authors.fullname: Zyeir
paper_authors.type: user
paper_authors.user: Ingra14m
paper_authors._id: 66cfdb63be74f57bd49d39bf
paper_authors.name: Bingchen Gong
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-08-29T02:22:32.743Z
paper_authors._id: 634938bcaa45636285efda4c
paper_authors.avatarUrl: /avatars/eb2f8d45be6b8396b446a0bbeaba7870.svg
paper_authors.fullname: Wenri
paper_authors.type: user
paper_authors.user: wenri
paper_authors._id: 66cfdb63be74f57bd49d39c0
paper_authors.name: Xiaoguang Han
paper_authors._id: 66cfdb63be74f57bd49d39c1
paper_authors.name: Sipeng Yang
paper_authors._id: 66cfdb63be74f57bd49d39c2
paper_authors.name: Xiaogang Jin
paper_title: Towards Realistic Example-based Modeling via 3D Gaussian Stitching
paper_summary: Using parts of existing models to rebuild new models, commonly termed as
  example-based modeling, is a classical methodology in the realm of computer
  graphics. Previous works mostly focus on shape composition, making them very
  hard to use for realistic composition of 3D objects captured from real-world
  scenes. This leads to combining multiple NeRFs into a single 3D scene to
  achieve seamless appearance blending. However, the current SeamlessNeRF method
  struggles to achieve interactive editing and harmonious stitching for
  real-world scenes due to its gradient-based strategy and grid-based
  representation. To this end, we present an example-based modeling method that
  combines multiple Gaussian fields in a point-based representation using
  sample-guided synthesis. Specifically, as for composition, we create a GUI to
  segment and transform multiple fields in real time, easily obtaining a
  semantically meaningful composition of models represented by 3D Gaussian
  Splatting (3DGS). For texture blending, due to the discrete and irregular
  nature of 3DGS, straightforwardly applying gradient propagation as SeamlssNeRF
  is not supported. Thus, a novel sampling-based cloning method is proposed to
  harmonize the blending while preserving the original rich texture and content.
  Our workflow consists of three steps: 1) real-time segmentation and
  transformation of a Gaussian model using a well-tailored GUI, 2) KNN analysis
  to identify boundary points in the intersecting area between the source and
  target models, and 3) two-phase optimization of the target model using
  sampling-based cloning and gradient constraints. Extensive experimental results
  validate that our approach significantly outperforms previous works in terms of
  realistic synthesis, demonstrating its practicality. More demos are available
  at https://ingra14m.github.io/gs_stitching_website.
paper_ai_summary: A novel example-based modeling method combines multiple Gaussian fields using a point-based representation for realistic 3D object composition and texture blending, outperforming previous approaches.
paper_ai_keywords: Gaussian fields point-based representation sample-guided synthesis 3D Gaussian Splatting (3DGS) KNN analysis sampling-based cloning gradient constraints