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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.03830 |
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| _version_ | 1866917583463120896 |
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| author | Yan, Guohang Pi, Jiahao Guo, Jianfei Luo, Zhaotong Dou, Min Deng, Nianchen Huang, Qiusheng Fu, Daocheng Wen, Licheng Cai, Pinlong Gao, Xing Cai, Xinyu Zhang, Bo Yang, Xuemeng Bai, Yeqi Zhou, Hongbin Shi, Botian |
| author_facet | Yan, Guohang Pi, Jiahao Guo, Jianfei Luo, Zhaotong Dou, Min Deng, Nianchen Huang, Qiusheng Fu, Daocheng Wen, Licheng Cai, Pinlong Gao, Xing Cai, Xinyu Zhang, Bo Yang, Xuemeng Bai, Yeqi Zhou, Hongbin Shi, Botian |
| contents | With deep learning and computer vision technology development, autonomous driving provides new solutions to improve traffic safety and efficiency. The importance of building high-quality datasets is self-evident, especially with the rise of end-to-end autonomous driving algorithms in recent years. Data plays a core role in the algorithm closed-loop system. However, collecting real-world data is expensive, time-consuming, and unsafe. With the development of implicit rendering technology and in-depth research on using generative models to produce data at scale, we propose OASim, an open and adaptive simulator and autonomous driving data generator based on implicit neural rendering. It has the following characteristics: (1) High-quality scene reconstruction through neural implicit surface reconstruction technology. (2) Trajectory editing of the ego vehicle and participating vehicles. (3) Rich vehicle model library that can be freely selected and inserted into the scene. (4) Rich sensors model library where you can select specified sensors to generate data. (5) A highly customizable data generation system can generate data according to user needs. We demonstrate the high quality and fidelity of the generated data through perception performance evaluation on the Carla simulator and real-world data acquisition. Code is available at https://github.com/PJLab-ADG/OASim. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving Yan, Guohang Pi, Jiahao Guo, Jianfei Luo, Zhaotong Dou, Min Deng, Nianchen Huang, Qiusheng Fu, Daocheng Wen, Licheng Cai, Pinlong Gao, Xing Cai, Xinyu Zhang, Bo Yang, Xuemeng Bai, Yeqi Zhou, Hongbin Shi, Botian Computer Vision and Pattern Recognition With deep learning and computer vision technology development, autonomous driving provides new solutions to improve traffic safety and efficiency. The importance of building high-quality datasets is self-evident, especially with the rise of end-to-end autonomous driving algorithms in recent years. Data plays a core role in the algorithm closed-loop system. However, collecting real-world data is expensive, time-consuming, and unsafe. With the development of implicit rendering technology and in-depth research on using generative models to produce data at scale, we propose OASim, an open and adaptive simulator and autonomous driving data generator based on implicit neural rendering. It has the following characteristics: (1) High-quality scene reconstruction through neural implicit surface reconstruction technology. (2) Trajectory editing of the ego vehicle and participating vehicles. (3) Rich vehicle model library that can be freely selected and inserted into the scene. (4) Rich sensors model library where you can select specified sensors to generate data. (5) A highly customizable data generation system can generate data according to user needs. We demonstrate the high quality and fidelity of the generated data through perception performance evaluation on the Carla simulator and real-world data acquisition. Code is available at https://github.com/PJLab-ADG/OASim. |
| title | OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.03830 |