Figure. The proposed framework of outlier removal by PointCVaR. Point risks are obtained by entering the noise sample into a trained classification model. Subsequently, an optimization problem is solved to minimize the tail of risk distribution, which leads to binary weights for noise point removal. The processed point cloud is utilized for classification.