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.

翻译：由于深度不够,估计单镜图像中物体准确的三维位置是一个棘手的问题。 先前的工作表明,利用物体的关键点预测限制来估计多个深度候选人,可以提高探测性能,但是,现有方法只能利用垂直边缘作为深度估计的预测限制。因此,这些方法只使用少量预测限制,产生足够的深度候选人,导致不准确的深度估计。在本文中,我们提出一种方法,利用任何方向边缘的密集预测限制。这样,我们使用更多的预测限制,产生相当深度的候选人。此外,我们提出一个图表,匹配加权模块,以合并深度候选人。提议的D方法D(高压探测器)在KITTI和WOD基准上达到最先进的性能。代码在https://github.com/BraveGroup/DCD发布。