Are Deep Point Cloud Classifiers Suffer from Out-of-distribution Overconfidence Issue?

3D point cloud perception using deep neural networks (DNNs) has been a trend for various application scenarios. However, the black-box nature of DNNs will bring many hidden risks as in the 2D image field. In this paper, we present a preliminary evaluation on the out-of-distribution (OOD) overconfidence issue of deep point cloud classifiers, which has been proven to exist in deep 2D image classifiers, i.e., OOD inputs will lead to overconfident predictions on predefined categories. We also investigate whether a simple thresholding baseline and two modern OOD detection solutions can handle the issue by detecting OOD samples. Extensive experiments with four representative deep point cloud classifiers train/evaluate on different in/out-of-distribution point clouds validate the severity and knottiness of the OOD overconfidence issue. Our investigation will provide the groundwork for future studies on handling the OOD overconfidence issue of DNN classifiers for 3D point clouds.