Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. (arXiv:2205.09373v1 [cs.CV])

As an inherently ill-posed problem, depth estimation from single images is
the most challenging part of monocular 3D object detection (M3OD). Many
existing methods rely on preconceived assumptions to bridge the missing spatial
information in monocular images, and predict a sole depth value for every
object of interest. However, these assumptions do not always hold in practical
applications. To tackle this problem, we propose a depth solving system that
fully explores the visual clues from the subtasks in M3OD and …

3d arxiv cv detection diversity