  Detecting and localizing objects in the real 3D space, which plays a crucial
role in scene understanding, is particularly challenging given only a monocular
image due to the geometric information loss during imagery projection. We
propose MonoGRNet for the amodal 3D object detection from a monocular image via
geometric reasoning in both the observed 2D projection and the unobserved depth
dimension. MonoGRNet decomposes the monocular 3D object detection task into
four sub-tasks including 2D object detection, instance-level depth estimation,
projected 3D center estimation and local corner regression. The task
decomposition significantly facilitates the monocular 3D object detection,
allowing the target 3D bounding boxes to be efficiently predicted in a single
forward pass, without using object proposals, post-processing or the
computationally expensive pixel-level depth estimation utilized by previous
methods. In addition, MonoGRNet flexibly adapts to both fully and weakly
supervised learning, which improves the feasibility of our framework in diverse
settings. Experiments are conducted on KITTI, Cityscapes and MS COCO datasets.
Results demonstrate the promising performance of our framework in various
scenarios.
