Summary of Lt3sd: Latent Trees For 3d Scene Diffusion, by Quan Meng et al.
LT3SD: Latent Trees for 3D Scene Diffusion
by Quan Meng, Lei Li, Matthias Nießner, Angela Dai
First submitted to arxiv on: 12 Sep 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 This paper presents LT3SD, a novel latent diffusion model for generating large-scale 3D scenes. While previous diffusion models excelled in object generation, they were limited when extended to complex scenes. The authors address this by introducing a latent tree representation that encodes geometry and detail at multiple scales. This allows them to learn a generative process in the latent space, modeling scene components at each resolution level. To synthesize large-scale scenes, LT3SD is trained on scene patches, enabling the generation of arbitrary-sized outputs through shared diffusion generation across multiple patches. The paper demonstrates the effectiveness of LT3SD for unconditional 3D scene generation and probabilistic completion for partial observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to create realistic 3D scenes using a computer model called LT3SD. Currently, there are models that can generate individual objects or small areas of a scene, but they struggle when it comes to creating large and complex scenes. The authors developed a new method that breaks down the scene into smaller parts and then combines them to create a realistic 3D environment. They tested this method on various types of scenes and showed that it can effectively generate large-scale scenes with different sizes and qualities. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Latent space
