  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.
