  3D object detection is an essential part of automated driving, and deep
neural networks (DNNs) have achieved state-of-the-art performance for this
task. However, deep models are notorious for assigning high confidence scores
to out-of-distribution (OOD) inputs, that is, inputs that are not drawn from
the training distribution. Detecting OOD inputs is challenging and essential
for the safe deployment of models. OOD detection has been studied extensively
for the classification task, but it has not received enough attention for the
object detection task, specifically LiDAR-based 3D object detection. In this
paper, we focus on the detection of OOD inputs for LiDAR-based 3D object
detection. We formulate what OOD inputs mean for object detection and propose
to adapt several OOD detection methods for object detection. We accomplish this
by our proposed feature extraction method. To evaluate OOD detection methods,
we develop a simple but effective technique of generating OOD objects for a
given object detection model. Our evaluation based on the KITTI dataset shows
that different OOD detection methods have biases toward detecting specific OOD
objects. It emphasizes the importance of combined OOD detection methods and
more research in this direction.
