Supplementary Material for the Paper “ Enriching Object Detection with 2 D-3 D Registration and Continuous Viewpoint Estimation ”

It this section, we provide qualitative examples and plots for the experiment “2D-3D Matching as an Object Detector” (Sect. 6.2 in the main paper). To recapitulate, we run our ensemble of NZ-WHO templates on the 3D Object Classes dataset [2], without the finetuning stage. Fig. 1 gives the corresponding detection average precision, average viewpoint precision, viewpoint confusion matrix and mean precision in pose estimation results. Specifically, we followed the detection and viewpoint estimation criteria of [4] where a detection is correct iff intersection over union is at least 0.5 and viewpoint estimation is correct iff detection is correct and azimuth of the viewpoint prediction falls into the correct viewpoint bin. Fig. 2 shows successful detection and viewpoint estimation results for car, Fig. 3 for bicycle. Fig. 4 and Fig. 5 show failure cases, which are mostly due to confused front and back views for cars, and slanted bicycle poses.