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
this https URL

本文提出了一种名为 DCD 的算法，通过利用物体的关键点投影约束来估计多个深度候选项，使用更多地投影约束和产生更多深度候选项，实现了更准确的深度估计。该算法在 KITTI 和 WOD 基准测试上取得了最先进的性能。

稠密约束深度估计器用于单目三维物体检测

Densely Constrained Depth Estimator for Monocular 3D Object Detection

Inferring 3D locations and shapes of multiple objects from a single 2D image
is a long-standing objective of computer vision. Most of the existing works
either predict one of these 3D properties or focus on solving both for a single
object. One fundamental challenge lies in how to learn an effective
representation of the image that is well-suited for 3D detection and
reconstruction. In this work, we propose to learn a regular grid of 3D voxel
features from the input image which is aligned with 3D scene space via a 3D
feature lifting operator. Based on the 3D voxel features, our novel
CenterNet-3D detection head formulates the 3D detection as keypoint detection
in the 3D space. Moreover, we devise an efficient coarse-to-fine reconstruction
module, including coarse-level voxelization and a novel local PCA-SDF shape
representation, which enables fine detail reconstruction and one order of
magnitude faster inference than prior methods. With complementary supervision
from both 3D detection and reconstruction, one enables the 3D voxel features to
be geometry and context preserving, benefiting both tasks.The effectiveness of
our approach is demonstrated through 3D detection and reconstruction in single
object and multiple object scenarios.

该研究提出了一种基于 3D 体素特征的中心点检测方法，其中包括一个有效的粗细重建模块，可从单个 2D 图像中检测和重建多个物体的 3D 位置和形状。