论文标题
在点云3D语义分段中进行分发检测的基准
A Benchmark for Out of Distribution Detection in Point Cloud 3D Semantic Segmentation
论文作者
论文摘要
自动驾驶（自动驾驶）等安全性应用程序使用深层神经网络（DNN）进行对象检测和分割。 DNN无法预测何时观察到分布（OOD）的输入导致灾难性后果。对现有的OOD检测方法进行了广泛的研究，以获取图像输入，但对于激光雷达输入并未探索太多。因此，在这项研究中，我们提出了两个数据集，用于在3D语义分割中基准测试OOD检测。我们使用了使用Randla-net的深层合奏和翻转版本产生的最大软磁概率和熵分数作为OOD分数。我们观察到，在两个数据集的AUROC得分方面，深层合奏在OOD检测中执行了翻转模型。
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.
