  We consider the problem of enriching current object detection systems with
veridical object sizes and relative depth estimates from a single image. There
are several technical challenges to this, such as occlusions, lack of
calibration data and the scale ambiguity between object size and distance.
These have not been addressed in full generality in previous work. Here we
propose to tackle these issues by building upon advances in object recognition
and using recently created large-scale datasets. We first introduce the task of
amodal bounding box completion, which aims to infer the the full extent of the
object instances in the image. We then propose a probabilistic framework for
learning category-specific object size distributions from available annotations
and leverage these in conjunction with amodal completion to infer veridical
sizes in novel images. Finally, we introduce a focal length prediction approach
that exploits scene recognition to overcome inherent scaling ambiguities and we
demonstrate qualitative results on challenging real-world scenes.
