  Research on monocular 3D object detection is being actively studied, and as a
result, performance has been steadily improving. However, 3D object detection
performance is significantly reduced when applied to a camera system different
from the system used to capture the training datasets. For example, a 3D
detector trained on datasets from a passenger car mostly fails to regress
accurate 3D bounding boxes for a camera mounted on a bus. In this paper, we
conduct extensive experiments to analyze the factors that cause performance
degradation. We find that changing the camera pose, especially camera
orientation, relative to the road plane caused performance degradation. In
addition, we propose a generalized 3D object detection method that can be
universally applied to various camera systems. We newly design a compensation
module that corrects the estimated 3D bounding box location and heading
direction. The proposed module can be applied to most of the recent 3D object
detection networks. It increases AP3D score (KITTI moderate, IoU $> 70\%$)
about 6-to-10-times above the baselines without additional training. Both
quantitative and qualitative results show the effectiveness of the proposed
method.
