MonoEdge: Monocular 3D Object Detection Using Local Perspectives. (arXiv:2301.01802v1 [cs.CV])

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
design a local perspective module to regress a newly defined variable 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 module does not rely on the pixel-wise size or position in
the image of the objects, therefore independent of the camera intrinsic
parameters. By plugging this module in existing monocular 3D object detection
frameworks, we incorporate the local perspective distortion with global
perspective effect for monocular 3D reasoning, and we demonstrate the
effectiveness and superior performance over strong baseline methods in multiple
datasets.