  Motivated by the detection of prohibited objects in carry-on luggage as a
part of avionic security screening, we develop a CNN-based object detection
approach for multi-view X-ray image data. Our contributions are two-fold.
First, we introduce a novel multi-view pooling layer to perform a 3D
aggregation of 2D CNN-features extracted from each view. To that end, our
pooling layer exploits the known geometry of the imaging system to ensure
geometric consistency of the feature aggregation. Second, we introduce an
end-to-end trainable multi-view detection pipeline based on Faster R-CNN, which
derives the region proposals and performs the final classification in 3D using
these aggregated multi-view features. Our approach shows significant accuracy
gains compared to single-view detection while even being more efficient than
performing single-view detection in each view.
