READ: Large-Scale Neural Scene Rendering for Autonomous Driving
Zhuopeng Li, Lu Li, Jianke Zhu
Abstract
With the development of advanced driver assistance systems~(ADAS) and autonomous vehicles, conducting experiments in various scenarios becomes an urgent need. Although having been capable of synthesizing photo-realistic street scenes, conventional image-to-image translation methods cannot produce coherent scenes due to the lack of 3D information. In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to generate large-scale driving scenes in real time on a PC through a variety of sampling schemes. In order to effectively represent driving scenarios, we propose an ω-net rendering network to learn neural descriptors from sparse point clouds. Our model can not only synthesize photo-realistic driving scenes but also stitch and edit them. The promising experimental results show that our model performs well in large-scale driving scenarios.
BibTeX
@article{Li_Li_Zhu_2023, title={READ: Large-Scale Neural Scene Rendering for Autonomous Driving}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/25238}, DOI={10.1609/aaai.v37i2.25238}, abstractNote={With the development of advanced driver assistance systems~(ADAS) and autonomous vehicles, conducting experiments in various scenarios becomes an urgent need. Although having been capable of synthesizing photo-realistic street scenes, conventional image-to-image translation methods cannot produce coherent scenes due to the lack of 3D information. In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to generate large-scale driving scenes in real time on a PC through a variety of sampling schemes. In order to effectively represent driving scenarios, we propose an ω-net rendering network to learn neural descriptors from sparse point clouds. Our model can not only synthesize photo-realistic driving scenes but also stitch and edit them. The promising experimental results show that our model performs well in large-scale driving scenarios.}, number={2}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Li, Zhuopeng and Li, Lu and Zhu, Jianke}, year={2023}, month={Jun.}, pages={1522-1529} }