# A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation Source: [A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:46:11.149Z Authors: Lokesh Veeramacheneni, Matias Valdenegro-Toro Published: 2022-11-11T14:33:51.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()