Summary of Magicdrive3d: Controllable 3d Generation For Any-view Rendering in Street Scenes, by Ruiyuan Gao et al.
MagicDrive3D: Controllable 3D Generation for Any-View Rendering in Street Scenes
by Ruiyuan Gao, Kai Chen, Zhihao Li, Lanqing Hong, Zhenguo Li, Qiang Xu
First submitted to arxiv on: 23 May 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 A novel pipeline called MagicDrive3D is introduced for controllable 3D street scene generation in unbounded scenarios like autonomous driving. This approach first trains a video generation model and then reconstructs from generated data, enabling easily controllable generation and static scene acquisition. The pipeline supports multi-condition control, including BEV maps, 3D objects, and text descriptions. To address minor errors in generated content, deformable Gaussian splatting with monocular depth initialization and appearance modeling are proposed to manage exposure discrepancies across viewpoints. MagicDrive3D is validated on the nuScenes dataset, generating diverse, high-quality 3D driving scenes that support any-view rendering and enhance downstream tasks like BEV segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MagicDrive3D is a new way to create 3D street scenes for self-driving cars. Normally, it’s hard to make fake 3D scenes because you need lots of real data to train the models. MagicDrive3D does things differently. It starts by training a model that can generate videos and then uses those generated videos to create realistic 3D scenes. This makes it easy to control what’s in the scene, like adding buildings or changing the weather. The model also fixes small mistakes in the generated content so the scenes look even more realistic. |
