  Monocular 3D object detection, with the aim of predicting the geometric
properties of on-road objects, is a promising research topic for the
intelligent perception systems of autonomous driving. Most state-of-the-art
methods follow a keypoint-based paradigm, where the keypoints of objects are
predicted and employed as the basis for regressing the other geometric
properties. In this work, a unified network named as FADNet is presented to
address the task of monocular 3D object detection. In contrast to previous
keypoint-based methods, we propose to divide the output modalities into
different groups according to the estimation difficulty of object properties.
Different groups are treated differently and sequentially associated by a
convolutional Gated Recurrent Unit. Another contribution of this work is the
strategy of depth hint augmentation. To provide characterized depth patterns as
hints for depth estimation, a dedicated depth hint module is designed to
generate row-wise features named as depth hints, which are explicitly
supervised in a bin-wise manner. The contributions of this work are validated
by conducting experiments and ablation study on the KITTI benchmark. Without
utilizing depth priors, post optimization, or other refinement modules, our
network performs competitively against state-of-the-art methods while
maintaining a decent running speed.
