  This paper addresses the problem of learning to estimate the depth of
detected objects given some measurement of camera motion (e.g., from robot
kinematics or vehicle odometry). We achieve this by 1) designing a recurrent
neural network (DBox) that estimates the depth of objects using a generalized
representation of bounding boxes and uncalibrated camera movement and 2)
introducing the Object Depth via Motion and Detection Dataset (ODMD). ODMD
training data are extensible and configurable, and the ODMD benchmark includes
21,600 examples across four validation and test sets. These sets include mobile
robot experiments using an end-effector camera to locate objects from the YCB
dataset and examples with perturbations added to camera motion or bounding box
data. In addition to the ODMD benchmark, we evaluate DBox in other monocular
application domains, achieving state-of-the-art results on existing driving and
robotics benchmarks and estimating the depth of objects using a camera phone.
