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.

通过对相机姿态变换的研究，我们发现 3D 物体检测性能在不同于训练数据所用相机系统的情况下明显降低。为了解决这个问题，我们提出了一个通用的 3D 物体检测方法，其中包括一个位置修正模块，用于校正估计的 3D 边界框位置和朝向，并在不进行额外训练的情况下，提高了 AP3D 得分（KITTI moderate，IoU > 70%）约 6 至 10 倍。定量和定性结果都验证了该方法的有效性。