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Updated: Jun 21, 2026
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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
Published on: December 3, 2013
Gamba: Marry Gaussian Splatting With Mamba for Single-View 3D Reconstruction
Summary
Gamba achieves millisecond-speed 3D reconstruction from a single image using a novel Mamba-based network and robust Gaussian constraints. This end-to-end model significantly accelerates 3D asset creation, outperforming existing methods.
Area of Science:
- Computer Vision
- Computer Graphics
- Artificial Intelligence
Background:
- Single-view 3D reconstruction is computationally intensive.
- Existing methods often require extensive training or optimization.
- Efficient and accurate 3D asset generation from limited input is a key challenge.
Purpose of the Study:
- To develop an end-to-end 3D reconstruction model capable of millisecond-speed generation.
- To introduce a novel architecture for efficient 3D Gaussian Splatting (3DGS) reconstruction.
- To enhance robustness by eliminating the need for 3D point cloud warmup supervision.
Main Methods:
- Introduced GambaFormer, a Mamba-based network for sequential 3DGS prediction with linear scalability.
- Developed radial mask constraints derived from multi-view masks for robust training.
- Trained the model on the Objaverse dataset and evaluated on the GSO Dataset.
Main Results:
- Gamba achieves end-to-end single-view 3D reconstruction using 3DGS.
- Reconstruction is completed in 0.05 seconds on a single NVIDIA A100 GPU, approximately 1,000x faster than optimization-based methods.
- Demonstrated competitive qualitative and quantitative generation capabilities compared to existing approaches.
Conclusions:
- Gamba presents a significant advancement in efficient and fast single-view 3D reconstruction.
- The Mamba-based architecture and novel constraints enable unprecedented speed and accuracy.
- This work paves the way for real-time 3D asset generation applications.
