title: 2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains
    for High-Fidelity Indoor Scene Reconstruction
summary: The reconstruction of indoor scenes remains challenging due to the inherent
  complexity of spatial structures and the prevalence of textureless regions.
  Recent advancements in 3D Gaussian Splatting have improved novel view synthesis
  with accelerated processing but have yet to deliver comparable performance in
  surface reconstruction. In this paper, we introduce 2DGS-Room, a novel method
  leveraging 2D Gaussian Splatting for high-fidelity indoor scene reconstruction.
  Specifically, we employ a seed-guided mechanism to control the distribution of
  2D Gaussians, with the density of seed points dynamically optimized through
  adaptive growth and pruning mechanisms. To further improve geometric accuracy,
  we incorporate monocular depth and normal priors to provide constraints for
  details and textureless regions respectively. Additionally, multi-view
  consistency constraints are employed to mitigate artifacts and further enhance
  reconstruction quality. Extensive experiments on ScanNet and ScanNet++ datasets
  demonstrate that our method achieves state-of-the-art performance in indoor
  scene reconstruction.
paper_authors._id: 6752e5756d713742ea3cfd4f
paper_authors.name: Wanting Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T15:55:29.889Z
paper_authors._id: 6752d040ffaeeb979dba4813
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/12ysrep6NnvtD8lwAG-pH.png
paper_authors.fullname: Wanting Zhang
paper_authors.type: user
paper_authors.user: Valentina-Zhang
paper_authors._id: 6752e5756d713742ea3cfd50
paper_authors.name: Haodong Xiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-09T16:01:09.273Z
paper_authors._id: 667559bad229a7f830aafb51
paper_authors.avatarUrl: /avatars/5cf1ace9433e274ec460fce99bdf6e06.svg
paper_authors.fullname: Haodong Xiang
paper_authors.type: user
paper_authors.user: XHD0612
paper_authors._id: 6752e5756d713742ea3cfd51
paper_authors.name: Zhichao Liao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-09T11:07:01.650Z
paper_authors._id: 66eb9d5d7f17088991ecbae2
paper_authors.avatarUrl: /avatars/10c77724829d4a1c65cafb4814614090.svg
paper_authors.fullname: Zhichao Liao
paper_authors.type: user
paper_authors.user: ChaosLiao
paper_authors._id: 6752e5756d713742ea3cfd52
paper_authors.name: Xiansong Lai
paper_authors._id: 6752e5756d713742ea3cfd53
paper_authors.name: Xinghui Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T15:55:33.428Z
paper_authors._id: 6752cd83ffaeeb979db974ae
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ci915Tdmv3uWbCqGy7LqL.png
paper_authors.fullname: Xinghui Li
paper_authors.type: user
paper_authors.user: Crayon-Shinchan
paper_authors._id: 6752e5756d713742ea3cfd54
paper_authors.name: Long Zeng
paper_title: 2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains
    for High-Fidelity Indoor Scene Reconstruction
paper_summary: The reconstruction of indoor scenes remains challenging due to the inherent
  complexity of spatial structures and the prevalence of textureless regions.
  Recent advancements in 3D Gaussian Splatting have improved novel view synthesis
  with accelerated processing but have yet to deliver comparable performance in
  surface reconstruction. In this paper, we introduce 2DGS-Room, a novel method
  leveraging 2D Gaussian Splatting for high-fidelity indoor scene reconstruction.
  Specifically, we employ a seed-guided mechanism to control the distribution of
  2D Gaussians, with the density of seed points dynamically optimized through
  adaptive growth and pruning mechanisms. To further improve geometric accuracy,
  we incorporate monocular depth and normal priors to provide constraints for
  details and textureless regions respectively. Additionally, multi-view
  consistency constraints are employed to mitigate artifacts and further enhance
  reconstruction quality. Extensive experiments on ScanNet and ScanNet++ datasets
  demonstrate that our method achieves state-of-the-art performance in indoor
  scene reconstruction.
paper_ai_keywords: 3D Gaussian Splatting 2D Gaussian Splatting seed-guided focusing adaptive growth mechanisms adaptive pruning mechanisms monocular depth priors textureless regions multi-view depth priors multi-view constraints reconstruction quality ScanNet dataset ScanNet++ dataset state-of-the-art performance