Enhancing Monocular 3D Object Detection with Occlusion Compensation

In the context of autonomous driving, occlusion significantly impacts the accuracy of monocular 3D object detection. To address this issue, a monocular 3D detection occlusion compensation algorithm is proposed to optimize the detection results affected by occlusion. The approach involves integrating an occlusion processing module into the 3D object detection pipeline. This module analyzes and handles occluded vehicles by leveraging 2D object detection results to infer occlusion relation-ships. Subsequently, the occluded local features associated with these relationships are captured by the local feature processing module and then input to the occlusion processing network. The resulting occlusion compensation offset is fed into the 3D object detection branch. Experimental results demonstrate that the method outperforms existing approaches on the KITTI 3D object detection benchmark.