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每天一篇论文PointTrackNet: An End-to-End Network for 3-D Object Detection and Tracking from Point Clouds
时间 2020-12-24
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PointTrackNet: An End-to-End Network for 3-D 摘要 摘要近年来,基于深度学习的多目标跟踪(MOT)框架在三维点云中得到了广泛的应用。大多数传统的跟踪方法使用滤波器(如Kalman滤波器或粒子滤波器)来预测时间序列中的目标位置,但是它们容易受到极端运动条件的影响,如突然刹车和转弯。在本文中,我们提出了一个端到端的三维目标检测和跟踪网络PointTrackN
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相关文章
1.
PointPillars: Fast Encoders for 3D Object Detection from Point Clouds
2.
PIXOR Real-time 3D Object Detection from Point Clouds
3.
【论文阅读】SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
4.
Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds
5.
Deep Hough Voting for 3D Object Detection in Point Clouds 论文笔记
6.
USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds 2019 论文笔记
7.
【3D计算机视觉】Deep Hough Voting for 3D Object Detection in Point Clouds
8.
#每天一篇论文 261/365 PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds
9.
#每天一篇论文 255/365 Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
10.
每天一篇论文(323/3)EGNet: Edge Guidance Network for Salient Object Detection
>>更多相关文章<<