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：随着三维感知技术的发展，面向三维点云的深度学习系统日益重要，尤其在自动驾驶等安全性至关重要的应用中。然而，当这些系统遭遇自然产生或恶意注入的噪声点云时，其可靠性问题也引发日益增长的关注。本文揭示了从简单背景噪声到可故意扭曲模型预测的恶意后门攻击等多种噪声形式对点云分类构成的挑战。尽管对优化点云去噪存在迫切需求，但当前作为去噪关键步骤的点离群点剔除方法严重依赖手工策略，且未能适应分类等高层任务。为解决该问题，我们提出一种创新的点离群点清洗方法，该方法充分利用下游分类模型能力。通过基于梯度的归因分析，我们定义了全新概念：点风险。受金融领域尾部风险最小化启发，我们将离群点剔除过程重新定义为优化问题，命名为PointCVaR。大量实验表明，所提技术不仅能鲁棒过滤多种点云离群点，还能持续显著增强现有基于鲁棒性的点云分类方法。