用GAN生成逼真雷达点云场景，替代真实数据采集。
Generative Adversarial Synthesis of Radar Point Cloud Scenes
- 基于PointNet++的GAN模型生成雷达点云场景
- 生成场景分类准确率达87%，接近真实数据表现
- 适合自动驾驶雷达验证与仿真数据生成
为验证汽车雷达性能，需要大量真实交通场景数据，但获取成本高。本文提出使用基于PointNet++的GAN模型生成雷达点云场景，作为真实数据采集和仿真方法的替代方案。通过二分类器评估生成场景与真实测试集的表现，结果表明该模型生成场景的分类准确率可达约87%，与真实数据集表现相当。
原文摘要 · Abstract (English)
For the validation and verification of automotive radars, datasets of realistic traffic scenarios are required, which, how ever, are laborious to acquire. In this paper, we introduce radar scene synthesis using GANs as an alternative to the real dataset acquisition and simulation-based approaches. We train a PointNet++ based GAN model to generate realistic radar point cloud scenes and use a binary classifier to evaluate the performance of scenes generated using this model against a test set of real scenes. We demonstrate that our GAN model achieves similar performance (~87%) to the real scenes test set.
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