Monocular 3D object detection is well-known to be a challenging vision task
due to the loss of depth information; attempts to recover depth using separate
image-only approaches lead to unstable and noisy depth estimates, harming 3D
detections. In this paper, we propose a novel keypoint-based approach for 3D
object detection and localization from a single RGB image. We build our
multi-branch model around 2D keypoint detection in images and complement it
with a conceptually simple geometric reasoning method. Our network performs in
an end-to-end manner, simultaneously and interdependently estimating 2D
characteristics, such as 2D bounding boxes, keypoints, and orientation, along
with full 3D pose in the scene. We fuse the outputs of distinct branches,
applying a reprojection consistency loss during training. The experimental
evaluation on the challenging KITTI dataset benchmark demonstrates that our
network achieves state-of-the-art results among other monocular 3D detectors.

本篇研究提出了一种基于关键点的方法，利用单个 RGB 图像进行三维目标检测和定位，该网络基于 2D 关键点检测和几何推理方法，同时估计 2D 特征和全局 3D 姿态，最终在 KITTI 数据集中达到了最好的性能表现。