With the growth of 3D sensing technology, deep learning system for 3D point clouds has become increasingly important, especially in applications like autonomous vehicles where safety is a primary concern. However, there are also growing concerns about the reliability of these systems when they encounter noisy point clouds, whether occurring naturally or introduced with malicious intent. This paper highlights the challenges of point cloud classification posed by various forms of noise, from simple background noise to malicious backdoor attacks that can intentionally skew model predictions. While there's an urgent need for optimized point cloud denoising, current point outlier removal approaches, an essential step for denoising, rely heavily on handcrafted strategies and are not adapted for higher-level tasks, such as classification. To address this issue, we introduce an innovative point outlier cleansing method that harnesses the power of downstream classification models. By employing gradient-based attribution analysis, we define a novel concept: point risk. Drawing inspiration from tail risk minimization in finance, we recast the outlier removal process as an optimization problem, named PointCVaR. Extensive experiments show that our proposed technique not only robustly filters diverse point cloud outliers but also consistently and significantly enhances existing robust methods for point cloud classification.

翻译：随着3D传感技术的发展，面向3D点云的深度学习系统变得日益重要，尤其在自动驾驶等安全关键应用中。然而，当这些系统遭遇自然产生或恶意引入的噪声点云时，其可靠性问题也日益引发关注。本文聚焦于点云分类中由多种噪声形式带来的挑战，涵盖从简单背景噪声到能够有意扭曲模型预测结果的恶意后门攻击。尽管优化点云去噪的需求十分迫切，但作为去噪关键步骤的现有离群点移除方法严重依赖人工设计策略，且无法适应分类等高层任务。为解决该问题，我们提出了一种创新的点云离群点清理方法，该方法充分利用了下游分类模型的能力。通过基于梯度的归因分析，我们定义了一个新概念：点风险。受金融领域尾风险最小化的启发，我们将离群点移除过程重新表述为优化问题，并命名为PointCVaR。大量实验表明，我们提出的技术不仅能稳健地过滤多种点云离群点，还能持续且显著地提升现有鲁棒点云分类方法的性能。