title: Gamba: Marry Gaussian Splatting with Mamba for single view 3D
    reconstruction
summary: 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.
paper_authors._id: 6604cb9c03c3c6badc631e85
paper_authors.name: Qiuhong Shen
paper_authors._id: 6604cb9c03c3c6badc631e86
paper_authors.name: Xuanyu Yi
paper_authors._id: 6604cb9c03c3c6badc631e87
paper_authors.name: Zike Wu
paper_authors._id: 6604cb9c03c3c6badc631e88
paper_authors.name: Pan Zhou
paper_authors._id: 6604cb9c03c3c6badc631e89
paper_authors.name: Hanwang Zhang
paper_authors._id: 6604cb9c03c3c6badc631e8a
paper_authors.name: Shuicheng Yan
paper_authors._id: 6604cb9c03c3c6badc631e8b
paper_authors.name: Xinchao Wang
paper_title: Gamba: Marry Gaussian Splatting with Mamba for single view 3D
    reconstruction
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Score Distillation Sampling Neural Radiance Fields 3D Gaussian splatting Mamba-based sequential network context-dependent reasoning OmniObject3D dataset