title: Generative Densification: Learning to Densify Gaussians for
    High-Fidelity Generalizable 3D Reconstruction
summary: Generalized feed-forward Gaussian models have achieved significant progress
  in sparse-view 3D reconstruction by leveraging prior knowledge from large
  multi-view datasets. However, these models often struggle to represent
  high-frequency details due to the limited number of Gaussians. While the
  densification strategy used in per-scene 3D Gaussian splatting (3D-GS)
  optimization can be adapted to the feed-forward models, it may not be ideally
  suited for generalized scenarios. In this paper, we propose Generative
  Densification, an efficient and generalizable method to densify Gaussians
  generated by feed-forward models. Unlike the 3D-GS densification strategy,
  which iteratively splits and clones raw Gaussian parameters, our method
  up-samples feature representations from the feed-forward models and generates
  their corresponding fine Gaussians in a single forward pass, leveraging the
  embedded prior knowledge for enhanced generalization. Experimental results on
  both object-level and scene-level reconstruction tasks demonstrate that our
  method outperforms state-of-the-art approaches with comparable or smaller model
  sizes, achieving notable improvements in representing fine details.
paper_authors._id: 675932605456a65fb50aeed5
paper_authors.name: Seungtae Nam
paper_authors.status: claimed_verified
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paper_authors.fullname: Seungtae
paper_authors.type: user
paper_authors.user: stnamjef
paper_authors._id: 675932605456a65fb50aeed6
paper_authors.name: Xiangyu Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-12T09:01:38.502Z
paper_authors._id: 634d10d4a252e0c53f99534c
paper_authors.avatarUrl: /avatars/d8a486e105769ffee59cf996a4f51686.svg
paper_authors.fullname: Sun Xiang yu
paper_authors.type: user
paper_authors.user: xysun
paper_authors._id: 675932605456a65fb50aeed7
paper_authors.name: Gyeongjin Kang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/904453c39c09aa51d3f6aa9fafb4a47d.svg
paper_authors.fullname: Gyeongjin Kang
paper_authors.type: user
paper_authors.user: Gynjn
paper_authors._id: 675932605456a65fb50aeed8
paper_authors.name: Younggeun Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-12T09:01:44.417Z
paper_authors._id: 66a4a1a7d8e85b03deddfa59
paper_authors.avatarUrl: /avatars/56dbec2101717ad9471e08a03ae51f0c.svg
paper_authors.fullname: Young geun Lee
paper_authors.type: user
paper_authors.user: LeeYG
paper_authors._id: 675932605456a65fb50aeed9
paper_authors.name: Seungjun Oh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-19T18:04:58.055Z
paper_authors._id: 672b66744efad666d2efb0c8
paper_authors.avatarUrl: /avatars/9c00a67e9d5b74694759849cca32b015.svg
paper_authors.fullname: Oh Seungjun
paper_authors.type: user
paper_authors.user: ohseungjun
paper_authors._id: 675932605456a65fb50aeeda
paper_authors.name: Eunbyung Park
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-12T09:01:55.354Z
paper_authors._id: 655e0141d36a195f663ee4b0
paper_authors.avatarUrl: /avatars/64609d6fd1581e6cf3e057ee569d69a1.svg
paper_authors.fullname: Eunbyung Park
paper_authors.type: user
paper_authors.user: epark
paper_title: Generative Densification: Learning to Densify Gaussians for
    High-Fidelity Generalizable 3D Reconstruction
paper_summary: Generalized feed-forward Gaussian models have achieved significant progress
  in sparse-view 3D reconstruction by leveraging prior knowledge from large
  multi-view datasets. However, these models often struggle to represent
  high-frequency details due to the limited number of Gaussians. While the
  densification strategy used in per-scene 3D Gaussian splatting (3D-GS)
  optimization can be adapted to the feed-forward models, it may not be ideally
  suited for generalized scenarios. In this paper, we propose Generative
  Densification, an efficient and generalizable method to densify Gaussians
  generated by feed-forward models. Unlike the 3D-GS densification strategy,
  which iteratively splits and clones raw Gaussian parameters, our method
  up-samples feature representations from the feed-forward models and generates
  their corresponding fine Gaussians in a single forward pass, leveraging the
  embedded prior knowledge for enhanced generalization. Experimental results on
  both object-level and scene-level reconstruction tasks demonstrate that our
  method outperforms state-of-the-art approaches with comparable or smaller model
  sizes, achieving notable improvements in representing fine details.
paper_ai_keywords: generalized feed-forward Gaussian models sparse-view 3D reconstruction large multi-view datasets dense-view 3D reconstruction per-scene 3D Gaussian splatting (3D-GS) Generative Densification feature representations fine Gaussians up-sampling embedded prior knowledge object-level reconstruction scene-level reconstruction state-of-the-art approaches