  Automatic prohibited object detection within 2D/3D X-ray Computed Tomography
(CT) has been studied in literature to enhance the aviation security screening
at checkpoints. Deep Convolutional Neural Networks (CNN) have demonstrated
superior performance in 2D X-ray imagery. However, there exists very limited
proof of how deep neural networks perform in materials detection within
volumetric 3D CT baggage screening imagery. We attempt to close this gap by
applying Deep Neural Networks in 3D contraband substance detection based on
their material signatures. Specifically, we formulate it as a 3D semantic
segmentation problem to identify material types for all voxels based on which
contraband materials can be detected. To this end, we firstly investigate 3D
CNN based semantic segmentation algorithms such as 3D U-Net and its variants.
In contrast to the original dense representation form of volumetric 3D CT data,
we propose to convert the CT volumes into sparse point clouds which allows the
use of point cloud processing approaches such as PointNet++ towards more
efficient processing. Experimental results on a publicly available dataset (NEU
ATR) demonstrate the effectiveness of both 3D U-Net and PointNet++ in materials
detection in 3D CT imagery for baggage security screening.
