In this work, we propose an efficient and accurate monocular 3D detection
framework in single shot. Most successful 3D detectors take the projection
constraint from the 3D bounding box to the 2D box as an important component.
Four edges of a 2D box provide only four constraints and the performance
deteriorates dramatically with the small error of the 2D detector. Different
from these approaches, our method predicts the nine perspective keypoints of a
3D bounding box in image space, and then utilize the geometric relationship of
3D and 2D perspectives to recover the dimension, location, and orientation in
3D space. In this method, the properties of the object can be predicted stably
even when the estimation of keypoints is very noisy, which enables us to obtain
fast detection speed with a small architecture. Training our method only uses
the 3D properties of the object without the need for external networks or
supervision data. Our method is the first real-time system for monocular image
3D detection while achieves state-of-the-art performance on the KITTI
benchmark. Code will be released at this https URL

该论文提出了一种高效准确的单目三维检测框架，通过预测图像空间中的九个三维边界框透视关键点，利用三维和二维透视的几何关系恢复三维空间中的物体属性，无需外部网络或监督数据，实现基于单目图像的实时三维检测。

RTM3D：自动驾驶中利用物体关键点进行单目实时三维检测

RTM3D: Real-time Monocular 3D Detection from Object Keypoints for  Autonomous Driving

In this paper, we introduce an anchor-box free and single shot instance
segmentation method, which is conceptually simple, fully convolutional and can
be used as a mask prediction module for instance segmentation, by easily
embedding it into most off-the-shelf detection methods. Our method, termed
PolarMask, formulates the instance segmentation problem as instance center
classification and dense distance regression in a polar coordinate. Moreover,
we propose two effective approaches to deal with sampling high-quality center
examples and optimization for dense distance regression, respectively, which
can significantly improve the performance and simplify the training process.
Without any bells and whistles, PolarMask achieves 32.9% in mask mAP with
single-model and single-scale training/testing on challenging COCO dataset. For
the first time, we demonstrate a much simpler and flexible instance
segmentation framework achieving competitive accuracy. We hope that the
proposed PolarMask framework can serve as a fundamental and strong baseline for
single shot instance segmentation tasks. Code is available at:
github.com/xieenze/PolarMask.

本文介绍了一种基于 PolarMask 的实例分割方法，它将实例分割问题作为极坐标系下的实例中心分类和密集距离回归，使用了两个有效的方法分别处理高质量实例中心的采样和密集距离回归的优化问题，可以在 COCO 数据集的单模型和单尺度训练 / 测试中取得 32.9% 的掩膜平均精度 (mAP)，是一种更简单、更灵活且表现优异的单次实例分割框架，为单次实例分割任务提供了一个强有力的基础。