[论文阅读]Improving Fine-Grained Recognition of Vehicles using 3D Bounding Boxes in Traffic Surveilla
这是一篇车辆细粒度识别的论文，它利用3D的bonding box来对3D包裹住的车辆对象拆分至一个平面，利用这个最终的imag进行训练，训练效果最好是在vgg16的分类精度达到84.13，追踪精度达到92.27。对于没有3D包围盒信息的，作者还提供了一种3D包围盒的预测算法。（尽管介绍这部分的写的也太不详细了，而且发布的源码来看也没有包含这部分，只包含了一份预测出的estimated_3DBB
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相关文章
- 1. 论文阅读：Multi-view Convolutional Neural Networks for 3D Shape Recognition
- 2. action recognition论文阅读
- 3. 论文阅读：CVPR2016 Paper list
- 4. 【3D 目标检测】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
- 5. 论文阅读：A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles
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- 8. 论文阅读：《Improving Content-based and Hybrid Music Recommendation using Deep Learning》
- 9. 论文阅读：Action recognition for human-marionette interaction.
- 10. 《论文阅读》ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes