  There have been attempts to detect 3D objects by fusion of stereo camera
images and LiDAR sensor data or using LiDAR for pre-training and only monocular
images for testing, but there have been less attempts to use only monocular
image sequences due to low accuracy. In addition, when depth prediction using
only monocular images, only scale-inconsistent depth can be predicted, which is
the reason why researchers are reluctant to use monocular images alone.
Therefore, we propose a method for predicting absolute depth and detecting 3D
objects using only monocular image sequences by enabling end-to-end learning of
detection networks and depth prediction networks. As a result, the proposed
method surpasses other existing methods in performance on the KITTI 3D dataset.
Even when monocular image and 3D LiDAR are used together during training in an
attempt to improve performance, ours exhibit is the best performance compared
to other methods using the same input. In addition, end-to-end learning not
only improves depth prediction performance, but also enables absolute depth
prediction, because our network utilizes the fact that the size of a 3D object
such as a car is determined by the approximate size.
