Gamba: Marry Gaussian Splatting with Mamba for single view 3D
  reconstruction
We tackle the challenge of efficiently reconstructing a 3D asset from a
single image with growing demands for automated 3D content creation pipelines.
Previous methods primarily rely on Score Distillation Sampling (SDS) and Neural
Radiance Fields (NeRF). Despite their significant success, these approaches
encounter practical limitations due to lengthy optimization and considerable
memory usage. In this report, we introduce Gamba, an end-to-end amortized 3D
reconstruction model from single-view images, emphasizing two main insights:
(1) 3D representation: leveraging a large number of 3D Gaussians for an
efficient 3D Gaussian splatting process; (2) Backbone design: introducing a
Mamba-based sequential network that facilitates context-dependent reasoning and
linear scalability with the sequence (token) length, accommodating a
substantial number of Gaussians. Gamba incorporates significant advancements in
data preprocessing, regularization design, and training methodologies. We
assessed Gamba against existing optimization-based and feed-forward 3D
generation approaches using the real-world scanned OmniObject3D dataset. Here,
Gamba demonstrates competitive generation capabilities, both qualitatively and
quantitatively, while achieving remarkable speed, approximately 0.6 second on a
single NVIDIA A100 GPU.
6604cb9c03c3c6badc631e85
Qiuhong Shen
claimed_verified
2025-06-17T07:29:48.007Z
643a6e89a856622f9788bf67
/avatars/419c0379f072295b27d4bfe2f8fb946d.svg
qiuhong shen
user
florinshum
6604cb9c03c3c6badc631e86
Xuanyu Yi
6604cb9c03c3c6badc631e87
Zike Wu
claimed_verified
2025-06-13T07:44:14.200Z
648058ff8c6a3b8f11f77893
/avatars/043c832314a8d6713af90d7c255fc2f2.svg
Wu Zike
user
Nickwzk
6604cb9c03c3c6badc631e88
Pan Zhou
6604cb9c03c3c6badc631e89
Hanwang Zhang
6604cb9c03c3c6badc631e8a
Shuicheng Yan
6604cb9c03c3c6badc631e8b
Xinchao Wang
Gamba: Marry Gaussian Splatting with Mamba for single view 3D
  reconstruction
We tackle the challenge of efficiently reconstructing a 3D asset from a
single image with growing demands for automated 3D content creation pipelines.
Previous methods primarily rely on Score Distillation Sampling (SDS) and Neural
Radiance Fields (NeRF). Despite their significant success, these approaches
encounter practical limitations due to lengthy optimization and considerable
memory usage. In this report, we introduce Gamba, an end-to-end amortized 3D
reconstruction model from single-view images, emphasizing two main insights:
(1) 3D representation: leveraging a large number of 3D Gaussians for an
efficient 3D Gaussian splatting process; (2) Backbone design: introducing a
Mamba-based sequential network that facilitates context-dependent reasoning and
linear scalability with the sequence (token) length, accommodating a
substantial number of Gaussians. Gamba incorporates significant advancements in
data preprocessing, regularization design, and training methodologies. We
assessed Gamba against existing optimization-based and feed-forward 3D
generation approaches using the real-world scanned OmniObject3D dataset. Here,
Gamba demonstrates competitive generation capabilities, both qualitatively and
quantitatively, while achieving remarkable speed, approximately 0.6 second on a
single NVIDIA A100 GPU.
Gamba is an end-to-end 3D reconstruction model that uses 3D Gaussian splatting and a Mamba-based sequential network for efficient single-view 3D asset generation.
Score Distillation Sampling Neural Radiance Fields 3D Gaussian splatting Mamba-based sequential network context-dependent reasoning OmniObject3D dataset