title: Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large
    Scene Reconstruction
summary: 3D Gaussian Splatting has demonstrated notable success in large-scale scene
  reconstruction, but challenges persist due to high training memory consumption
  and storage overhead. Hybrid representations that integrate implicit and
  explicit features offer a way to mitigate these limitations. However, when
  applied in parallelized block-wise training, two critical issues arise since
  reconstruction accuracy deteriorates due to reduced data diversity when
  training each block independently, and parallel training restricts the number
  of divided blocks to the available number of GPUs. To address these issues, we
  propose Momentum-GS, a novel approach that leverages momentum-based
  self-distillation to promote consistency and accuracy across the blocks while
  decoupling the number of blocks from the physical GPU count. Our method
  maintains a teacher Gaussian decoder updated with momentum, ensuring a stable
  reference during training. This teacher provides each block with global
  guidance in a self-distillation manner, promoting spatial consistency in
  reconstruction. To further ensure consistency across the blocks, we incorporate
  block weighting, dynamically adjusting each block's weight according to its
  reconstruction accuracy. Extensive experiments on large-scale scenes show that
  our method consistently outperforms existing techniques, achieving a 12.8%
  improvement in LPIPS over CityGaussian with much fewer divided blocks and
  establishing a new state of the art. Project page:
  https://jixuan-fan.github.io/Momentum-GS_Page/
paper_authors._id: 67570717fbdf1d2e45e30865
paper_authors.name: Jixuan Fan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-09T15:58:00.388Z
paper_authors._id: 6426989c6e8a0ba53a989a5e
paper_authors.avatarUrl: /avatars/a56f23fdb8bf1d94739a442de9a0e04e.svg
paper_authors.fullname: Jixuan Fan
paper_authors.type: user
paper_authors.user: inlcude
paper_authors._id: 67570717fbdf1d2e45e30866
paper_authors.name: Wanhua Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-09T15:58:02.051Z
paper_authors._id: 658bb7e47459b6e471b9d2e6
paper_authors.avatarUrl: /avatars/efd8051b468b4dbcb5d149479de67c58.svg
paper_authors.fullname: Wanhua Li
paper_authors.type: user
paper_authors.user: EthanTaylor
paper_authors._id: 67570717fbdf1d2e45e30867
paper_authors.name: Yifei Han
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-09T15:58:39.142Z
paper_authors._id: 6441194dbd0c9726529629cf
paper_authors.avatarUrl: /avatars/8ee47dbdbe7fee83c44b76d288767dce.svg
paper_authors.fullname: Yifei Han
paper_authors.type: user
paper_authors.user: yifever
paper_authors._id: 67570717fbdf1d2e45e30868
paper_authors.name: Yansong Tang
paper_title: Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large
    Scene Reconstruction
paper_summary: 3D Gaussian Splatting has demonstrated notable success in large-scale scene
  reconstruction, but challenges persist due to high training memory consumption
  and storage overhead. Hybrid representations that integrate implicit and
  explicit features offer a way to mitigate these limitations. However, when
  applied in parallelized block-wise training, two critical issues arise since
  reconstruction accuracy deteriorates due to reduced data diversity when
  training each block independently, and parallel training restricts the number
  of divided blocks to the available number of GPUs. To address these issues, we
  propose Momentum-GS, a novel approach that leverages momentum-based
  self-distillation to promote consistency and accuracy across the blocks while
  decoupling the number of blocks from the physical GPU count. Our method
  maintains a teacher Gaussian decoder updated with momentum, ensuring a stable
  reference during training. This teacher provides each block with global
  guidance in a self-distillation manner, promoting spatial consistency in
  reconstruction. To further ensure consistency across the blocks, we incorporate
  block weighting, dynamically adjusting each block's weight according to its
  reconstruction accuracy. Extensive experiments on large-scale scenes show that
  our method consistently outperforms existing techniques, achieving a 12.8%
  improvement in LPIPS over CityGaussian with much fewer divided blocks and
  establishing a new state of the art. Project page:
  https://jixuan-fan.github.io/Momentum-GS_Page/
paper_ai_summary: A novel momentum-based self-distillation method for 3D Gaussian Splatting enhances reconstruction accuracy and efficiency in large-scale scene reconstruction by addressing memory and storage challenges.
paper_ai_keywords: 3D Gaussian Splatting implicit features explicit features momentum-based self-distillation Gaussian decoder spatial consistency block weighting LPIPS CityGaussian