NVAutoNet：面向自动驾驶的快速且精确的360° 3D视觉感知
NVAutoNet: Fast and Accurate 360$^{\circ}$ 3D Visual Perception For Self Driving
AI总结：
NVAutoNet是一个面向自动驾驶的BEV感知网络，通过高效卷积骨干、TensorRT优化及线性层与查找表变换，在DRIVE Orin上以53 FPS实现障碍物、可行驶区域和停车位的精确3D感知，并具备对传感器安装偏差和不同车型的强适应性。
摘要：
实现鲁棒且实时的3D感知是自动驾驶汽车的基础。虽然现有的大多数3D感知方法优先考虑检测精度，但它们往往忽视了计算效率、车载芯片部署友好性、对传感器安装偏差的鲁棒性以及对不同车型的适应性等关键方面。为应对这些挑战，我们提出了NVAutoNet：一个专门为自动驾驶汽车定制的鸟瞰图（BEV）感知网络。NVAutoNet以同步相机图像为输入，预测障碍物、可行驶区域和停车位等3D信号。NVAutoNet架构的核心（图像骨干网络和BEV骨干网络）依赖于高效的卷积网络，并使用TensorRT针对高性能进行了优化。更重要的是，我们的图像到BEV变换采用简单的线性层和BEV查找表，确保了快速的推理速度。在一个大规模专有数据集上训练后，NVAutoNet持续实现了较高的感知精度，在NVIDIA DRIVE Orin SoC上以每秒53帧的速度出色运行。值得注意的是，NVAutoNet对因不同车型引起的传感器安装偏差表现出鲁棒性。此外，NVAutoNet在适应不同车辆类型方面表现出色，这得益于低成本的模型微调流程，加快了兼容性调整。
英文摘要：
Achieving robust and real-time 3D perception is fundamental for autonomous vehicles. While most existing 3D perception methods prioritize detection accuracy, they often overlook critical aspects such as computational efficiency, onboard chip deployment friendliness, resilience to sensor mounting deviations, and adaptability to various vehicle types. To address these challenges, we present NVAutoNet: a specialized Bird's-Eye-View (BEV) perception network tailored explicitly for automated vehicles. NVAutoNet takes synchronized camera images as input and predicts 3D signals like obstacles, freespaces, and parking spaces. The core of NVAutoNet's architecture (image and BEV backbones) relies on efficient convolutional networks, optimized for high performance using TensorRT. More importantly, our image-to-BEV transformation employs simple linear layers and BEV look-up tables, ensuring rapid inference speed. Trained on an extensive proprietary dataset, NVAutoNet consistently achieves elevated perception accuracy, operating remarkably at 53 frames per second on the NVIDIA DRIVE Orin SoC. Notably, NVAutoNet demonstrates resilience to sensor mounting deviations arising from diverse car models. Moreover, NVAutoNet excels in adapting to varied vehicle types, facilitated by inexpensive model fine-tuning procedures that expedite compatibility adjustments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
