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Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving #24

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DeepTecher opened this issue Mar 25, 2019 · 1 comment
Open

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving #24

DeepTecher opened this issue Mar 25, 2019 · 1 comment
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CVPR 2019 目标检测|Object Detection 论文速递 无人驾驶最新相关论文

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@DeepTecher
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Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving,CVPR 2019, 3D物体检测

提交日期:2019-03-18(2018-12-18 v1)
团队：康奈尔大学
作者：Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
摘要：3D物体检测是自动驾驶中的基本任务。如果从精确但昂贵的LiDAR技术获得3D输入数据，则技术具有高度准确的检测率。迄今为止，基于较便宜的单眼或立体图像数据的方法导致精度显着降低 - 这种差距通常归因于基于图像的深度估计不良。然而，在本文中，我们认为数据表示（而不是其质量）占据了差异的大部分。考虑到卷积神经网络的内部工作原理，我们建议将基于图像的深度图转换为伪LiDAR表示 - 基本上模仿LiDAR信号。通过这种表示，我们可以应用不同的现有基于LiDAR的检测算法。在KITTI基准测试中，我们的方法在现有的基于图像的性能方面取得了SOTA的改进 -提高了30米范围内物体的检测精度，从先前SOTA的22％到现在的74％ 。在提交时，我们的算法在KITTI 3D对象检测基于立体图像的方法的排行榜上为第一。
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DeepTecher commented Mar 25, 2019 •
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代码：mileyan/pseudo_lidar，暂未公布代码
项目链接：mileyan.github.io/pseudo_lidar/

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