Monocular 3D object detection task aims to predict the 3D bounding boxes of
objects based on monocular RGB images. Since the location recovery in 3D space
is quite difficult on account of absence of depth information, this paper
proposes a novel unified framework which decomposes the detection problem into
a structured polygon prediction task and a depth recovery task. Different from
the widely studied 2D bounding boxes, the proposed novel structured polygon in
the 2D image consists of several projected surfaces of the target object.
Compared to the widely-used 3D bounding box proposals, it is shown to be a
better representation for 3D detection. In order to inversely project the
predicted 2D structured polygon to a cuboid in the 3D physical world, the
following depth recovery task uses the object height prior to complete the
inverse projection transformation with the given camera projection matrix.
Moreover, a fine-grained 3D box refinement scheme is proposed to further
rectify the 3D detection results. Experiments are conducted on the challenging
KITTI benchmark, in which our method achieves state-of-the-art detection
accuracy.

该研究提出了一种新的统一框架，将单眼 RGB 图像中的目标检测问题分解为结构化多边形预测任务和深度恢复任务，并使用对象高度先验进行反向投影变换，进一步纠正 3D 检测结果并取得了最先进的检测准确度。