文章标题:Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving 文章链接:https://arxiv.org/abs/1812.07179 代码链接:https://github.com/mileyan/pseudo_lidar 名词解释: Pseudo-LiDAR(伪激光点云),即通过双目或者单目视觉获取的深度信息,经过投影变换,将每...
题目:Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving(自动驾驶) 作者:Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger 论文链接:https://arxiv.org/abs/1812.07179 项目链接:https://mileyan.github...
来自康奈尔大学的"Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving". Pseudo-LiDAR主要探讨了为什么Image-based 3D Perception与LiDAR-based 3D Perception之间存在较大的gap,并且提出了bridge this gap的解决方案。
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Dr 项目链接:https://mileyan.github.io/pseudo_lidar/ 代码链接:https://github.com/mileyan/pseudo_lidar 摘要 3D目标检测是一个重要的任务在自动驾驶领域。最近高精度检测率技...
课件成果介绍pseudo lidar from visual depth estimation bridging the gap 3d object detection for autonomous driving.pdf,Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving Yan Wang, Wei-Lun Chao, Divyan
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving byYan Wang,Wei-Lun Chao,Divyansh Garg,Bharath Hariharan,Mark CampbellandKilian Q. Weinberger Citation @inproceedings{wang2019pseudo, title={Pseudo-LiDAR from Visual Depth Estimation: Bridging the...
Pseudo-LiDAR from Visual Depth Estimation:Bridging the Gap in 3D Object Detection for Autonomous Driving 作者:Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, and Kilian Q. Weinb... 查看原文 「干货」2019 CVPR 无人驾驶资源帖 ...
了一种算法结构,可以学习点云的3D数据结构,该算法可以直接处理3D激光点云数据。 7、论文名称:Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving 代码:https://github.com/mileyan/pseudo_lidar. 作者智能...
对于精确并且昂贵的激光点云数据来说当前的3D检测算法具有很高的检测精度。然而到目前为止,使用廉价的单目相机或者立体相机数据的检测算法仍然很难达到较高的精度,出现这种差距的主要原因是基于图像数据算法在深度估计上存在较大的误差。然而,在这篇论文中,认为造成这种差异的主要原因不是数据的质量,而是数据的表现形式...
Pseudo-lidar from visual depth estimation: Bridging the gap in 3D object detection for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 8445–8453. [Google Scholar] You, Y.; Wang, Y.; ...