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Safety-critical applications like autonomous driving use Deep Neural Networks(DNNs) for object detection and segmentation. The DNNs fail to predict whenthey observe an Out-of-Distribution (OOD) input leading to catastrophicconsequences. Existing OOD detection methods were extensively studied for imageinputs but have not been explored much for LiDAR inputs. So in this study, weproposed two datasets for benchmarking OOD detection in 3D semanticsegmentation. We used Maximum Softmax Probability and Entropy scores generatedusing Deep Ensembles and Flipout versions of RandLA-Net as OOD scores. Weobserved that Deep Ensembles out perform Flipout model in OOD detection withgreater AUROC scores for both datasets.

A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation