Monocular 3D Object Detection Using Depth Fusion

Abstract It is an important task to estimate a 3D bounding box from monocular images for autonomous driving. However, the pictures do not have distance information, so it difficult acquire accurate results. For sake of solving trouble low accuracy image in target detection because lacking improved three-dimensional algorithm based on GUPNet and neural network was proposed promote precision detection. First, geometric method by GUPNet, depth, uncertainty are obtained direct regression using network. According difference two methods, parameter α introduced, their depth scores uncertainty. score , methods fused get final depth. Test results prove that promotes average KITTI data set simple, medium, cases.