  Safety-critical applications like autonomous driving use Deep Neural Networks
(DNNs) for object detection and segmentation. The DNNs fail to predict when
they observe an Out-of-Distribution (OOD) input leading to catastrophic
consequences. Existing OOD detection methods were extensively studied for image
inputs but have not been explored much for LiDAR inputs. So in this study, we
proposed two datasets for benchmarking OOD detection in 3D semantic
segmentation. We used Maximum Softmax Probability and Entropy scores generated
using Deep Ensembles and Flipout versions of RandLA-Net as OOD scores. We
observed that Deep Ensembles out perform Flipout model in OOD detection with
greater AUROC scores for both datasets.
