  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.
