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We propose a novel approach for monocular 3D object detection by leveraginglocal perspective effects of each object. While the global perspective effectshown as size and position variations has been exploited for monocular 3Ddetection extensively, the local perspectives has long been overlooked. Wedesign a local perspective module to regress a newly defined variable namedkeyedge-ratios as the parameterization of the local shape distortion to accountfor the local perspective, and derive the object depth and yaw angle from it.Theoretically, this module does not rely on the pixel-wise size or position inthe image of the objects, therefore independent of the camera intrinsicparameters. By plugging this module in existing monocular 3D object detectionframeworks, we incorporate the local perspective distortion with globalperspective effect for monocular 3D reasoning, and we demonstrate theeffectiveness and superior performance over strong baseline methods in multipledatasets.

MonoEdge: Monocular 3D Object Detection Using Local Perspectives