  Visual perception of the objects in a 3D environment is a key to successful
performance in autonomous driving and simultaneous localization and mapping
(SLAM). In this paper, we present a real time approach for estimating the
distances to multiple objects in a scene using only a single-shot image. Given
a 2D Bounding Box (BBox) and object parameters, a 3D distance to the object can
be calculated directly using 3D reprojection; however, such methods are prone
to significant errors because an error from the 2D detection can be amplified
in 3D. In addition, it is also challenging to apply such methods to a real-time
system due to the computational burden. In the case of the traditional
multi-object detection methods, %they mostly pay attention to existing works
have been developed for specific tasks such as object segmentation or 2D BBox
regression. These methods introduce the concept of anchor BBox for elaborate 2D
BBox estimation, and predictors are specialized and trained for specific 2D
BBoxes. In order to estimate the distances to the 3D objects from a single 2D
image, we introduce the notion of \textit{anchor distance} based on an object's
location and propose a method that applies the anchor distance to the
multi-object detector structure. We let the predictors catch the distance prior
using anchor distance and train the network based on the distance. The
predictors can be characterized to the objects located in a specific distance
range. By propagating the distance prior using a distance anchor to the
predictors, it is feasible to perform the precise distance estimation and
real-time execution simultaneously. The proposed method achieves about 30 FPS
speed, and shows the lowest RMSE compared to the existing methods.
