Realistic simulation is key to enabling safe and scalable development of % self-driving vehicles. A core component is simulating the sensors so that the entire autonomy system can be tested in simulation. Sensor simulation involves modeling traffic participants, such as vehicles, with high quality appearance and articulated geometry, and rendering them in real time. The self-driving industry has typically employed artists to build these assets. However, this is expensive, slow, and may not reflect reality. Instead, reconstructing assets automatically from sensor data collected in the wild would provide a better path to generating a diverse and large set with good real-world coverage. Nevertheless, current reconstruction approaches struggle on in-the-wild sensor data, due to its sparsity and noise. To tackle these issues, we present CADSim, which combines part-aware object-class priors via a small set of CAD models with differentiable rendering to automatically reconstruct vehicle geometry, including articulated wheels, with high-quality appearance. Our experiments show our method recovers more accurate shapes from sparse data compared to existing approaches. Importantly, it also trains and renders efficiently. We demonstrate our reconstructed vehicles in several applications, including accurate testing of autonomy perception systems.

翻译：逼真的仿真是实现自动驾驶车辆安全且可规模化发展的关键。其核心组成部分是对传感器进行仿真，从而使得整个自动驾驶系统能够在仿真环境中接受测试。传感器仿真涉及对交通参与者（例如车辆）进行建模，要求具备高质量的外观和可动几何结构，并实现实时渲染。自动驾驶行业通常依赖艺术家来构建这些资产，但这种方式成本高昂、速度缓慢，且可能无法反映真实情况。相比之下，从野外采集的传感器数据中自动重建资产，将为生成多样化、大规模且真实世界覆盖良好的资产集提供更优路径。然而，由于野外传感器数据的稀疏性和噪声问题，现有重建方法难以有效处理此类数据。为解决上述挑战，我们提出了CADSim，该方法通过少量CAD模型引入部件感知的对象类别先验，并结合可微渲染技术，自动重建车辆几何结构（包括可动车轮）及高质量外观。实验表明，与现有方法相比，我们的方法能从稀疏数据中恢复更准确的形状。重要的是，该方法在训练和渲染方面也具备高效性。我们展示了重建车辆在多个应用场景中的效果，包括自动驾驶感知系统的精确测试。