Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage
  Gaussian Splats
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
671155f28f706a2159128b02
Chen Ziwen
claimed_verified
2024-10-18T08:13:31.879Z
668ddf7fe990292e5fcb2ab3
/avatars/0f9c0f845c40a662a5045261b22607c6.svg
Ziwen Chen
user
arthurhero
671155f28f706a2159128b03
Hao Tan
671155f28f706a2159128b04
Kai Zhang
admin_assigned
2024-10-18T16:04:41.959Z
63e0a50242591dda0b9dca5c
https://cdn-avatars.huggingface.co/v1/production/uploads/63e0a50242591dda0b9dca5c/c7cBPEBWQDFYimfGnO_SI.png
Kai Zhang
user
drogozhang
671155f28f706a2159128b05
Sai Bi
admin_assigned
2024-10-18T16:04:34.774Z
66303560597dc7c535168f07
/avatars/a69d52a4a198c6c8451c1d137031bf07.svg
Sai Bi
user
saibi
671155f28f706a2159128b06
Fujun Luan
671155f28f706a2159128b07
Yicong Hong
admin_assigned
2024-10-18T16:04:13.211Z
654ca66fe1671abcbc50a6e4
https://cdn-avatars.huggingface.co/v1/production/uploads/654ca66fe1671abcbc50a6e4/VqcnC17YcqC9Hk65_LxtE.jpeg
Yicong Hong
user
YicongHong
671155f28f706a2159128b08
Li Fuxin
671155f28f706a2159128b09
Zexiang Xu
admin_assigned
2024-10-18T16:04:18.449Z
65565b1b8616d8d740abb976
/avatars/c245b19ed05292bf1bf4a9d8cf2a409c.svg
Zexiang Xu
user
zexiangxu
Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage
  Gaussian Splats
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
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.
Mamba2 blocks transformer blocks token merging Gaussian pruning feed-forward models DL3DV-140 Tanks and Temples