论文标题
再次回到：学习为真实世界应用模拟雷达数据
There and Back Again: Learning to Simulate Radar Data for Real-World Applications
论文作者
论文摘要
模拟现实的雷达数据有可能显着加速数据驱动的雷达处理方法。但是，由于臭名昭著的复杂图像形成过程，它充满了困难。在这里，我们建议学习一个雷达传感器模型，该模型能够基于模拟的高程图综合忠实的雷达观测值。特别是，我们采用一种对抗性方法来从未对齐的雷达示例中学习前向传感器模型。此外，建模向后模型会鼓励输出通过周期性的一致性标准保持与世界状态保持一致。向后模型进一步限制，以预测实际雷达数据的高度图，这些数据是通过相应的激光扫描获得的部分测量基础的。两种模型均经过联合优化训练。我们通过评估在现实世界部署中纯粹对模拟数据训练的下游分割模型来证明方法的功效。这将在完全基于真实数据的同一模型的四个百分点内实现性能。
Simulating realistic radar data has the potential to significantly accelerate the development of data-driven approaches to radar processing. However, it is fraught with difficulty due to the notoriously complex image formation process. Here we propose to learn a radar sensor model capable of synthesising faithful radar observations based on simulated elevation maps. In particular, we adopt an adversarial approach to learning a forward sensor model from unaligned radar examples. In addition, modelling the backward model encourages the output to remain aligned to the world state through a cyclical consistency criterion. The backward model is further constrained to predict elevation maps from real radar data that are grounded by partial measurements obtained from corresponding lidar scans. Both models are trained in a joint optimisation. We demonstrate the efficacy of our approach by evaluating a down-stream segmentation model trained purely on simulated data in a real-world deployment. This achieves performance within four percentage points of the same model trained entirely on real data.
