Summary of Diorama: Unleashing Zero-shot Single-view 3d Indoor Scene Modeling, by Qirui Wu et al.
Diorama: Unleashing Zero-shot Single-view 3D Indoor Scene Modeling
by Qirui Wu, Denys Iliash, Daniel Ritchie, Manolis Savva, Angel X. Chang
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper presents Diorama, a zero-shot open-world system that reconstructs 3D scenes from single-view RGB observations without requiring end-to-end training or human annotations. The system decomposes the problem into subtasks: architecture reconstruction, 3D shape retrieval, object pose estimation, and scene layout optimization. Diorama is trained on CAD objects and generalizes well to unseen objects and domains. The authors demonstrate the feasibility of their approach by evaluating it on both synthetic and real-world data, showing significant performance improvements over prior work. They also show generalization to internet images and the text-to-scene task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers can build 3D scenes from just one picture. Right now, building these scenes requires a lot of training data or careful control over what’s in the picture. The authors created a new system called Diorama that can do this without needing any extra help. They broke down the task into smaller steps and developed special solutions for each part. This lets Diorama work really well on both pretend and real pictures. It even works when shown new pictures or asked to create scenes based on text descriptions. |
Keywords
» Artificial intelligence » Generalization » Optimization » Pose estimation » Zero shot
