title: SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian
    Splatting
summary: We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform
  pose-free and 3D prior-free generalizable 3D reconstruction from unposed
  multi-view images. These settings are inherently ill-posed due to the lack of
  ground-truth data, learned geometric information, and the need to achieve
  accurate 3D reconstruction without finetuning, making it difficult for
  conventional methods to achieve high-quality results. Our model addresses these
  challenges by effectively integrating explicit 3D representations with
  self-supervised depth and pose estimation techniques, resulting in reciprocal
  improvements in both pose accuracy and 3D reconstruction quality. Furthermore,
  we incorporate a matching-aware pose estimation network and a depth refinement
  module to enhance geometry consistency across views, ensuring more accurate and
  stable 3D reconstructions. To present the performance of our method, we
  evaluated it on large-scale real-world datasets, including RealEstate10K, ACID,
  and DL3DV. SelfSplat achieves superior results over previous state-of-the-art
  methods in both appearance and geometry quality, also demonstrates strong
  cross-dataset generalization capabilities. Extensive ablation studies and
  analysis also validate the effectiveness of our proposed methods. Code and
  pretrained models are available at https://gynjn.github.io/selfsplat/
paper_authors._id: 67493353a82ab7120ff46295
paper_authors.name: Gyeongjin Kang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-29T09:33:26.851Z
paper_authors._id: 64aa93e799639cc312795ea8
paper_authors.avatarUrl: /avatars/904453c39c09aa51d3f6aa9fafb4a47d.svg
paper_authors.fullname: Gyeongjin Kang
paper_authors.type: user
paper_authors.user: Gynjn
paper_authors._id: 67493353a82ab7120ff46296
paper_authors.name: Jisang Yoo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-12T08:22:46.587Z
paper_authors._id: 675a7d9e06ae644140a57788
paper_authors.avatarUrl: /avatars/765ac0e5b443200bd3618399f31a7116.svg
paper_authors.fullname: Jisang Yoo
paper_authors.type: user
paper_authors.user: jisang1528
paper_authors._id: 67493353a82ab7120ff46297
paper_authors.name: Jihyeon Park
paper_authors._id: 67493353a82ab7120ff46298
paper_authors.name: Seungtae Nam
paper_authors._id: 67493353a82ab7120ff46299
paper_authors.name: Hyeonsoo Im
paper_authors._id: 67493353a82ab7120ff4629a
paper_authors.name: Sangheon Shin
paper_authors._id: 67493353a82ab7120ff4629b
paper_authors.name: Sangpil Kim
paper_authors._id: 67493353a82ab7120ff4629c
paper_authors.name: Eunbyung Park
paper_title: SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian
    Splatting
paper_summary: We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform
  pose-free and 3D prior-free generalizable 3D reconstruction from unposed
  multi-view images. These settings are inherently ill-posed due to the lack of
  ground-truth data, learned geometric information, and the need to achieve
  accurate 3D reconstruction without finetuning, making it difficult for
  conventional methods to achieve high-quality results. Our model addresses these
  challenges by effectively integrating explicit 3D representations with
  self-supervised depth and pose estimation techniques, resulting in reciprocal
  improvements in both pose accuracy and 3D reconstruction quality. Furthermore,
  we incorporate a matching-aware pose estimation network and a depth refinement
  module to enhance geometry consistency across views, ensuring more accurate and
  stable 3D reconstructions. To present the performance of our method, we
  evaluated it on large-scale real-world datasets, including RealEstate10K, ACID,
  and DL3DV. SelfSplat achieves superior results over previous state-of-the-art
  methods in both appearance and geometry quality, also demonstrates strong
  cross-dataset generalization capabilities. Extensive ablation studies and
  analysis also validate the effectiveness of our proposed methods. Code and
  pretrained models are available at https://gynjn.github.io/selfsplat/
paper_ai_keywords: SelfSplat 3D Gaussian Splatting model pose-free 3D prior-free Generalizable 3D reconstruction Multi-view images Ill-posed Ground-truth data Learned geometric information Accurate 3D reconstruction Finetuning Explicit 3D representations Self-supervised depth estimation Self-supervised pose estimation Reciprocal improvements Pose accuracy 3D reconstruction quality Matching-aware pose estimation network Depth refinement module Geometry consistency RealEstate10K ACID DL3DV Explicit geometry quality Cross-dataset generalization