Point clouds are extensively employed in a variety of real-world applications such as robotics, autonomous driving and augmented reality. Despite the recent success of point cloud neural networks, especially for safety-critical tasks, it is essential to also ensure the robustness of the model. A typical way to assess a model's robustness is through adversarial attacks, where test-time examples are generated based on gradients to deceive the model. While many different defense mechanisms are studied in 2D, studies on 3D point clouds have been relatively limited in the academic field. Inspired from PointDP, which denoises the network inputs by diffusion, we propose Point Cloud Layerwise Diffusion (PCLD), a layerwise diffusion based 3D point cloud defense strategy. Unlike PointDP, we propagated the diffusion denoising after each layer to incrementally enhance the results. We apply our defense method to different types of commonly used point cloud models and adversarial attacks to evaluate its robustness. Our experiments demonstrate that the proposed defense method achieved results that are comparable to or surpass those of existing methodologies, establishing robustness through a novel technique. Code is available at https://github.com/batuceng/diffusion-layer-robustness-pc.

翻译：点云被广泛应用于机器人、自动驾驶和增强现实等实际场景。尽管点云神经网络近期取得了成功，特别是在安全关键任务中，但确保模型的鲁棒性至关重要。评估模型鲁棒性的典型方法是通过对抗攻击，即基于梯度生成测试时样本来欺骗模型。尽管在二维领域已研究多种防御机制，但三维点云的相关研究在学术界相对有限。受PointDP（通过扩散对网络输入进行去噪）启发，我们提出点云逐层扩散（PCLD），一种基于逐层扩散的三维点云防御策略。与PointDP不同，我们在每一层后传播扩散去噪以逐步增强结果。我们将所提防御方法应用于不同类型的常见点云模型和对抗攻击，以评估其鲁棒性。实验表明，所提防御方法达到了与现有方法相当或更优的结果，通过一种新技术建立了鲁棒性。代码见https://github.com/batuceng/diffusion-layer-robustness-pc。