Depth-Assisted Joint Detection Network For Monocular 3d Object Detection

Abstract

In the past few years, monocular 3D object detection has attracted increasing attention due to the merit of low cost and wide range of applications. In this paper, a depth-assisted joint detection network (MonoDAJD) is proposed for monocular 3D object detection. Specifically, a consistency-aware joint detection mechanism is proposed to jointly detect objects in the image and depth map, and exploit the localization information from the depth detection stream to optimize the detection results. To obtain more accurate 3D bounding boxes, an orientation-embedded NMS is designed by introducing the orientation confidence prediction and embedding the orientation confidence into the traditional NMS. Experimental results on the widely used KITTI benchmark demonstrate that the proposed method achieves promising performance compared with the state-of-the-art monocular 3D object detection methods.