摘要翻译：
三维目标检测与分类是自动驾驶中的一个关键问题。激光雷达传感器用于提供周围环境的三维点云重建，而实时检测三维物体包围盒的任务仍然是一个强大的算法挑战。本文在二维透视图像空间中一次回归元体系结构成功的基础上，将其扩展到从激光雷达点云中生成面向对象的三维包围盒。我们的主要贡献是扩展了YOLO v2的损失函数，将偏航角、笛卡尔坐标系下的三维箱体中心和箱体高度作为一个直接回归问题包括在内。这种提法实现了实时性能，这对于自动驾驶是必不可少的。我们的结果显示了KITTI基准上有希望的数字，在Titan X GPU上实现了实时性能（40 fps）。
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英文标题：
《YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection
  from LiDAR Point Cloud》
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作者：
Waleed Ali, Sherif Abdelkarim, Mohamed Zahran, Mahmoud Zidan and Ahmad
  El Sallab
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Object detection and classification in 3D is a key task in Automated Driving (AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding environment, while the task of 3D object bounding box detection in real time remains a strong algorithmic challenge. In this paper, we build on the success of the one-shot regression meta-architecture in the 2D perspective image space and extend it to generate oriented 3D object bounding boxes from LiDAR point cloud. Our main contribution is in extending the loss function of YOLO v2 to include the yaw angle, the 3D box center in Cartesian coordinates and the height of the box as a direct regression problem. This formulation enables real-time performance, which is essential for automated driving. Our results are showing promising figures on KITTI benchmark, achieving real-time performance (40 fps) on Titan X GPU.
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PDF链接：
https://arxiv.org/pdf/1808.0235