Summary of Sseditor: Controllable Mask-to-scene Generation with Diffusion Model, by Haowen Zheng and Yanyan Liang
SSEditor: Controllable Mask-to-Scene Generation with Diffusion Model
by Haowen Zheng, Yanyan Liang
First submitted to arxiv on: 19 Nov 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 paper proposes a controllable Semantic Scene Editor (SSEditor) for generating specified target categories in 3D semantic scene generation. The approach uses a two-stage diffusion-based framework that first trains a 3D scene autoencoder and then a mask-conditional diffusion model for customizable generation. A geometric-semantic fusion module is introduced to enhance the model’s ability to learn geometric and semantic information. SSEditor outperforms previous approaches in terms of controllability, flexibility, and quality of semantic scene generation and reconstruction on SemanticKITTI, CarlaSC, and Occ-3D Waymo datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SSEditor is a new way to create 3D scenes that can be customized by specifying what’s inside. Right now, most methods make things up as they go along, which isn’t very helpful if you want to change something. SSEditor changes this by allowing for more control over the scene generation process. It does this by using two stages: first, it learns how to compress and then expand 3D scenes, and second, it uses a special module that combines geometric and semantic information. This results in scenes that are not only visually accurate but also contain the correct objects with their correct positions, sizes, and categories. |
Keywords
» Artificial intelligence » Autoencoder » Diffusion » Diffusion model » Mask
