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.

翻译：随着深度学习与计算机视觉技术的发展，自动驾驶为提升交通安全与效率提供了新方案。构建高质量数据集的重要性不言而喻，特别是在近年来端到端自动驾驶算法兴起的背景下，数据在算法闭环系统中扮演着核心角色。然而，采集真实世界数据成本高昂、耗时且存在安全隐患。基于隐式渲染技术的发展和利用生成模型大规模生产数据的深入研究，我们提出OASim——一种基于隐式神经渲染的开放自适应仿真器与自动驾驶数据生成器。它具有以下特性：（1）通过神经隐式表面重建技术实现高质量场景重建；（2）支持自车与参与车辆的轨迹编辑；（3）丰富的车辆模型库，可自由选择并插入场景；（4）丰富的传感器模型库，可选择指定传感器生成数据；（5）高度可定制化的数据生成系统，可根据用户需求生成数据。我们通过Carla仿真器及真实数据采集的感知性能评估，验证了生成数据的高质量与高保真度。代码已开源至https://github.com/PJLab-ADG/OASim。