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Visual perception of the objects in a 3D environment is a key to successfulperformance in autonomous driving and simultaneous localization and mapping(SLAM). In this paper, we present a real time approach for estimating thedistances to multiple objects in a scene using only a single-shot image. Givena 2D Bounding Box (BBox) and object parameters, a 3D distance to the object canbe calculated directly using 3D reprojection; however, such methods are proneto significant errors because an error from the 2D detection can be amplifiedin 3D. In addition, it is also challenging to apply such methods to a real-timesystem due to the computational burden. In the case of the traditionalmulti-object detection methods, %they mostly pay attention to existing workshave been developed for specific tasks such as object segmentation or 2D BBoxregression. These methods introduce the concept of anchor BBox for elaborate 2DBBox estimation, and predictors are specialized and trained for specific 2DBBoxes. In order to estimate the distances to the 3D objects from a single 2Dimage, we introduce the notion of \textit{anchor distance} based on an object'slocation and propose a method that applies the anchor distance to themulti-object detector structure. We let the predictors catch the distance priorusing anchor distance and train the network based on the distance. Thepredictors can be characterized to the objects located in a specific distancerange. By propagating the distance prior using a distance anchor to thepredictors, it is feasible to perform the precise distance estimation andreal-time execution simultaneously. The proposed method achieves about 30 FPSspeed, and shows the lowest RMSE compared to the existing methods.

Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot