Fig. 1. The structure of our proposed 3D concealed object detector for AMMW holographic images

Fig. 2. Projection of the AMMW holographic image (a) AMMW holographic image, (b) the resulting 2D front view of performing projection along the Z axis of the holographic image in Fig. 2(a), (c) the shape and size changes caused by projecting a 3D object into 2D views

Fig. 3. The structure of our proposed input module

Fig. 4. The distribution of the number of points in the bounding box in 3D point clouds and 2D front images

Fig. 5. The structure of our proposed 3D feature extractor

Fig. 6. The structure of our proposed output module

Fig. 7. Comparison of localization and detection performance for different networks (a) Recall as a function of IOU threshold, (b) PR curve under IOU = 0.5

Fig. 8. Qualitative detection results of different networks, where the red bounding boxes denote the ground-truth, and the yellow bounding boxes denote the predicted results (a)-(d) Our proposed method, (e) RPN, (f) Faster RCNN, (g) RetinaNet, (h) TridentNet

Fig. 9. F1-score under different thresholds of the confidence