  Augmenting RGB data with measured depth has been shown to improve the
performance of a range of tasks in computer vision including object detection
and semantic segmentation. Although depth sensors such as the Microsoft Kinect
have facilitated easy acquisition of such depth information, the vast majority
of images used in vision tasks do not contain depth information. In this paper,
we show that augmenting RGB images with estimated depth can also improve the
accuracy of both object detection and semantic segmentation. Specifically, we
first exploit the recent success of depth estimation from monocular images and
learn a deep depth estimation model. Then we learn deep depth features from the
estimated depth and combine with RGB features for object detection and semantic
segmentation. Additionally, we propose an RGB-D semantic segmentation method
which applies a multi-task training scheme: semantic label prediction and depth
value regression. We test our methods on several datasets and demonstrate that
incorporating information from estimated depth improves the performance of
object detection and semantic segmentation remarkably.
