title: Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage
    Gaussian Splats
summary: We propose Long-LRM, a generalizable 3D Gaussian reconstruction model that is
  capable of reconstructing a large scene from a long sequence of input images.
  Specifically, our model can process 32 source images at 960x540 resolution
  within only 1.3 seconds on a single A100 80G GPU. Our architecture features a
  mixture of the recent Mamba2 blocks and the classical transformer blocks which
  allowed many more tokens to be processed than prior work, enhanced by efficient
  token merging and Gaussian pruning steps that balance between quality and
  efficiency. Unlike previous feed-forward models that are limited to processing
  1~4 input images and can only reconstruct a small portion of a large scene,
  Long-LRM reconstructs the entire scene in a single feed-forward step. On
  large-scale scene datasets such as DL3DV-140 and Tanks and Temples, our method
  achieves performance comparable to optimization-based approaches while being
  two orders of magnitude more efficient. Project page:
  https://arthurhero.github.io/projects/llrm
paper_authors._id: 671155f28f706a2159128b02
paper_authors.name: Chen Ziwen
paper_authors.status: claimed_verified
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paper_authors.fullname: Ziwen Chen
paper_authors.type: user
paper_authors.user: arthurhero
paper_authors._id: 671155f28f706a2159128b03
paper_authors.name: Hao Tan
paper_authors._id: 671155f28f706a2159128b04
paper_authors.name: Kai Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: Kai Zhang
paper_authors.type: user
paper_authors.user: drogozhang
paper_authors._id: 671155f28f706a2159128b05
paper_authors.name: Sai Bi
paper_authors.status: admin_assigned
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paper_authors.fullname: Sai Bi
paper_authors.type: user
paper_authors.user: saibi
paper_authors._id: 671155f28f706a2159128b06
paper_authors.name: Fujun Luan
paper_authors._id: 671155f28f706a2159128b07
paper_authors.name: Yicong Hong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-18T16:04:13.211Z
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paper_authors.fullname: Yicong Hong
paper_authors.type: user
paper_authors.user: YicongHong
paper_authors._id: 671155f28f706a2159128b08
paper_authors.name: Li Fuxin
paper_authors._id: 671155f28f706a2159128b09
paper_authors.name: Zexiang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-18T16:04:18.449Z
paper_authors._id: 65565b1b8616d8d740abb976
paper_authors.avatarUrl: /avatars/c245b19ed05292bf1bf4a9d8cf2a409c.svg
paper_authors.fullname: Zexiang Xu
paper_authors.type: user
paper_authors.user: zexiangxu
paper_title: Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage
    Gaussian Splats
paper_summary: We propose Long-LRM, a generalizable 3D Gaussian reconstruction model that is
  capable of reconstructing a large scene from a long sequence of input images.
  Specifically, our model can process 32 source images at 960x540 resolution
  within only 1.3 seconds on a single A100 80G GPU. Our architecture features a
  mixture of the recent Mamba2 blocks and the classical transformer blocks which
  allowed many more tokens to be processed than prior work, enhanced by efficient
  token merging and Gaussian pruning steps that balance between quality and
  efficiency. Unlike previous feed-forward models that are limited to processing
  1~4 input images and can only reconstruct a small portion of a large scene,
  Long-LRM reconstructs the entire scene in a single feed-forward step. On
  large-scale scene datasets such as DL3DV-140 and Tanks and Temples, our method
  achieves performance comparable to optimization-based approaches while being
  two orders of magnitude more efficient. Project page:
  https://arthurhero.github.io/projects/llrm
paper_ai_summary: Long-LRM reconstructs large 3D scenes efficiently from multiple images using a combination of Mamba2 and transformer blocks, achieving performance comparable to optimization-based methods.
paper_ai_keywords: Mamba2 blocks transformer blocks token merging Gaussian pruning feed-forward models DL3DV-140 Tanks and Temples