Efficient 3D Object Detection of Indoor Scenes Based on RGB-D Video Stream

Abstract

: For indoor object detection, the input complex scenes often have some defects such as incomplete RGB-D scanning data or mutual occlusion of its objects. Meanwhile, due to the limitations of frame in the RGB-D video stream. Using SUN RGB-D dataset to train the object detection network of key frame, the detection result of proposed method is accurate, and the overall detection time is greatly reduced if com-paring with the VoteNet based frame-by-frame detection scheme. Experimental results demonstrate that proposed method is effective and efficient.