  Monocular depth estimation has been actively studied in fields such as robot
vision, autonomous driving, and 3D scene understanding. Given a sequence of
color images, unsupervised learning methods based on the framework of
Structure-From-Motion (SfM) simultaneously predict depth and camera relative
pose. However, dynamically moving objects in the scene violate the static world
assumption, resulting in inaccurate depths of dynamic objects. In this work, we
propose a new method to address such dynamic object movements through monocular
3D object detection. Specifically, we first detect 3D objects in the images and
build the per-pixel correspondence of the dynamic pixels with the detected
object pose while leaving the static pixels corresponding to the rigid
background to be modeled with camera motion. In this way, the depth of every
pixel can be learned via a meaningful geometry model. Besides, objects are
detected as cuboids with absolute scale, which is used to eliminate the scale
ambiguity problem inherent in monocular vision. Experiments on the KITTI depth
dataset show that our method achieves State-of-The-Art performance for depth
estimation. Furthermore, joint training of depth, camera motion and object pose
also improves monocular 3D object detection performance. To the best of our
knowledge, this is the first work that allows a monocular 3D object detection
network to be fine-tuned in a self-supervised manner.
