Abstract
Most of the object detection technique in computer vision domain use 2D data or 3D data as input. However, in some finding object scenarios, we only need to know which direction and how far it is, that is, knowing the position on the ground plane is enough. Hence we propose method leverage both of the benefit from 2D and 3D detection -- Bird’s Eye View Detection. By using depth image and some special encoding method, we can gather the information like height, the angle of surface normal, texture appearance of each point in the scene. Next step we use that information to create the Bird’s Eye View image for indoor object detection. The benefits of Bird’s Eye View Detection is that we could leverage the strength of 2D detection model (Faster-RCNN or Yolo v2), and avoid high memory and time consumption when training with the 3D detector. We use challenging indoor real scene dataset NYUv2 dataset and ScanNet dataset as input for the experiment, and get 31.7% mAP result on 19 most common indoor objects.