MonoEdge: Monocular 3D Object Detection Using Local Perspectives

We propose a novel approach for monocular 3D object detection by leveraging local perspective effects of each object. While the global perspective effect shown as size and position variations has been exploited for monocular 3D detection extensively, the local perspectives has long been overlooked. We propose a new regression target named keyedge-ratios as the parameterization of the local shape distortion to account for the local perspective, and derive the object depth and yaw angle from it. Theoretically, this approach does not rely on the absolute size or position of the objects in the image, therefore independent of the camera intrinsic parameters. This approach provides a new perspective for monocular 3D reasoning and can be plugged in flexibly to existing monocular 3D object detection frameworks. We demonstrate effectiveness and superior performance over strong baseline methods in multiple datasets.