Summary of Efficient 3d Shape Generation Via Diffusion Mamba with Bidirectional Ssms, by Shentong Mo
Efficient 3D Shape Generation via Diffusion Mamba with Bidirectional SSMs
by Shentong Mo
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 DiM-3D architecture addresses the scalability issues of traditional diffusion transformers (DiT) with self-attention mechanisms in generating 3D shapes. By forgoing attention mechanisms, DiM-3D maintains linear complexity with respect to sequence length, resulting in fast inference times and reduced computational demands. This is achieved through the selective state space approach of the Mamba architecture, which enables efficient long sequence handling. The model’s empirical results on the ShapeNet benchmark demonstrate its ability to generate high-fidelity and diverse 3D shapes, outperforming existing models in tasks like 3D point cloud completion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DiM-3D is a new way of generating 3D shapes that makes it faster and more efficient. It’s designed specifically for this task and gets around the problem of traditional methods getting too slow as they try to generate more detailed shapes. The results show that DiM-3D can create high-quality, diverse shapes, and it does this better than other models at tasks like filling in missing parts of a 3D shape. |
Keywords
» Artificial intelligence » Attention » Diffusion » Inference » Self attention
