用烟雾和水雾模拟干扰，测试激光雷达3D检测模型的鲁棒性。
A New Adversarial Perspective for LiDAR-based 3D Object Detection
- 构建烟雾与水雾的点云生成模型PCS-GAN，模拟真实环境干扰。
- 在不同位置注入干扰后，检测模型识别率显著下降。
- 适合自动驾驶安全评估与模型抗干扰能力研究者参考。
自动驾驶车辆依赖激光雷达（LiDAR）进行环境感知与决策。然而，在复杂环境下确保其安全性和可靠性仍是重大挑战。为此，我们构建了一个真实世界数据集ROLiD，包含随机物体（水雾和烟雾）的激光雷达点云。本文提出一种新型对抗视角，设计攻击框架，利用水雾与烟雾模拟环境干扰。具体地，提出基于运动与内容分解的生成对抗网络PCS-GAN，生成点云序列以模拟随机物体分布；并结合范围图像（Range Image）实现的模拟激光扫描特性，分析在不同位置引入随机物体扰动对目标车辆的影响。大量实验表明，基于随机物体的对抗扰动能有效欺骗车辆检测系统，显著降低3D目标检测模型的识别率。
原文摘要 · Abstract (English)
Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke. In this paper, we introduce a novel adversarial perspective by proposing an attack framework that utilizes water mist and smoke to simulate environmental interference. Specifically, we propose a point cloud sequence generation method using a motion and content decomposition generative adversarial network named PCS-GAN to simulate the distribution of random objects. Furthermore, leveraging the simulated LiDAR scanning characteristics implemented with Range Image, we examine the effects of introducing random object perturbations at various positions on the target vehicle. Extensive experiments demonstrate that adversarial perturbations based on random objects effectively deceive vehicle detection and reduce the recognition rate of 3D object detection models.
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