【阅读】-- PillarFlow : End-to-end Birds-eye-view Flow Estimation for Autonomous Driving

时间 2021-01-02

标签 Motion Detection Motion Flow 繁體版

原文原文链接

IROS 2020 ， Toyota Research Institute 的一篇文章。本文提出了一种基于激光雷达的场景运动估计器，该估计器与目标检测分离，因此具有互补性。我们的方法将两个连续的完整LIDAR点云扫描作为输入。每个LIDAR扫描都编码为学习的特征向量的离散化2D BeV表示（也称为“Pillars”）。然后，基于PWC-Net工作改编了光流网络，以局部匹配两个连续的Be

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1. 论文阅读CVPR2019——Stereo R-CNN based 3D Object Detection for Autonomous Driving
2. 解读ECCV2020：Pillar-based Object Detection for Autonomous Driving
3. 【论文阅读】A survey of deep learning techniques for autonomous driving
4. Pillar-based Object Detection for Autonomous Driving
5. YUVMultiNet Real-time YUV multi-task CNN for autonomous driving
6. 《Virtual to Real Reinforcement Learning for Autonomous Driving》翻译
7. 论文笔记_CV_AD_Visual Perception for Autonomous Driving
8. Multi-View 3D Object Detection Network for Autonomous Driving
9. Stereo R-CNN based 3D Object Detection for Autonomous Driving
10. [论文解读]Multi-View 3D Object Detection Network for Autonomous Driving

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