Summary of Renderworld: World Model with Self-supervised 3d Label, by Ziyang Yan et al.
RenderWorld: World Model with Self-Supervised 3D Label
by Ziyang Yan, Wenzhen Dong, Yihua Shao, Yuhang Lu, Liu Haiyang, Jingwen Liu, Haozhe Wang, Zhe Wang, Yan Wang, Fabio Remondino, Yuexin Ma
First submitted to arxiv on: 17 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 The proposed RenderWorld framework is a vision-only end-to-end autonomous driving system that leverages a self-supervised Img2Occ Module for generating 3D occupancy labels, encoded by AM-VAE. The system uses Gaussian Splatting to represent 3D scenes and render 2D images, achieving state-of-the-art performance in 4D occupancy forecasting and motion planning from an autoregressive world model. Unlike LiDAR-vision fusion, RenderWorld is more cost-effective, and its purely visual approach enhances reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RenderWorld is a new way to help cars drive without using special sensors like LiDAR. Instead, it uses cameras to see the world and make decisions. The system works by creating 3D maps of the environment and predicting what might happen next. This helps the car decide where to go and how to get there safely. RenderWorld is better than other methods because it’s more reliable and cost-effective. |
Keywords
» Artificial intelligence » Autoregressive » Self supervised
