Summary of Gamba: Marry Gaussian Splatting with Mamba For Single View 3d Reconstruction, by Qiuhong Shen et al.
Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction
by Qiuhong Shen, Zike Wu, Xuanyu Yi, Pan Zhou, Hanwang Zhang, Shuicheng Yan, Xinchao Wang
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Gamba model efficiently reconstructs a 3D asset from a single image at millisecond speed. The existing methods for single-image 3D reconstruction are primarily based on Score Distillation Sampling (SDS) with Neural 3D representations, but they encounter practical limitations due to lengthy optimizations and significant memory consumption. The Gamba model is an end-to-end 3D reconstruction model from a single-view image, emphasizing two main insights: Efficient Backbone Design and Robust Gaussian Constraints. It introduces a Mamba-based GambaFormer network to model 3D Gaussian Splatting (3DGS) reconstruction as sequential prediction with linear scalability of token length. The model is trained on Objaverse and assessed against existing optimization-based and feed-forward 3D reconstruction approaches on the GSO Dataset. Experimental results demonstrate its competitive generation capabilities both qualitatively and quantitatively, highlighting its remarkable speed: Gamba completes reconstruction within 0.05 seconds on a single NVIDIA A100 GPU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gamba is a new way to create 3D models from just one picture. Right now, this process takes a long time because computers need to do lots of complicated math problems. The Gamba team found ways to make the math faster and more efficient, so they can make 3D models really quickly – in just milliseconds! This is super useful for things like video games, movies, and even self-driving cars. |
Keywords
» Artificial intelligence » Distillation » Optimization » Token
