تقرير
Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection
| العنوان: | Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection |
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| المؤلفون: | Kösel, Michael, Schreiber, Marcel, Ulrich, Michael, Gläser, Claudius, Dietmayer, Klaus |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | LiDAR-based 3D object detection has become an essential part of automated driving due to its ability to localize and classify objects precisely in 3D. However, object detectors face a critical challenge when dealing with unknown foreground objects, particularly those that were not present in their original training data. These out-of-distribution (OOD) objects can lead to misclassifications, posing a significant risk to the safety and reliability of automated vehicles. Currently, LiDAR-based OOD object detection has not been well studied. We address this problem by generating synthetic training data for OOD objects by perturbing known object categories. Our idea is that these synthetic OOD objects produce different responses in the feature map of an object detector compared to in-distribution (ID) objects. We then extract features using a pre-trained and fixed object detector and train a simple multilayer perceptron (MLP) to classify each detection as either ID or OOD. In addition, we propose a new evaluation protocol that allows the use of existing datasets without modifying the point cloud, ensuring a more authentic evaluation of real-world scenarios. The effectiveness of our method is validated through experiments on the newly proposed nuScenes OOD benchmark. The source code is available at https://github.com/uulm-mrm/mmood3d. Comment: Accepted for publication at the 2024 35th IEEE Intelligent Vehicles Symposium (IV 2024), June 2-5, 2024, in Jeju Island, Korea |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.15879 |
| رقم الأكسشن: | edsarx.2404.15879 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |