  Estimating accurate 3D locations of objects from monocular images is a
challenging problem because of lacking depth. Previous work shows that
utilizing the object's keypoint projection constraints to estimate multiple
depth candidates boosts the detection performance. However, the existing
methods can only utilize vertical edges as projection constraints for depth
estimation. So these methods only use a small number of projection constraints
and produce insufficient depth candidates, leading to inaccurate depth
estimation. In this paper, we propose a method that utilizes dense projection
constraints from edges of any direction. In this way, we employ much more
projection constraints and produce considerable depth candidates. Besides, we
present a graph matching weighting module to merge the depth candidates. The
proposed method DCD (Densely Constrained Detector) achieves state-of-the-art
performance on the KITTI and WOD benchmarks. Code is released at
https://github.com/BraveGroup/DCD.
