车辆检测“Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monoc”
Deep Many Task，同时进行车辆检测，部件定位，可视化特征描述及3D维度估计。基于coarse-to-fine的目标proposal结构提升检测性能。Deep MANTA可以定位不可见的车辆部位。 应用 3D车辆定位和方向估计可用于估计车辆速度和方向。 论文第一个贡献是使用车辆特征点编码3D车辆信息，车辆是刚性的，可通过回归的方法预测隐藏的部分。结合3D数据集，将3D点投影到2D图像中的
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相关文章
- 1. 车辆2D/3D--Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis
- 2. 论文阅读：Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis
- 3. 3D车辆检测 | Deep MANTA 算法笔记
- 4. 车辆检测--A Closer Look at Faster R-CNN for Vehicle Detection
- 5. 车辆检索“LEARNING A REPRESSION NETWORK FOR PRECISE VEHICLE SEARCH”
- 6. 3d fully convolutional network for vehicle detection in point cloud
- 7. 论文：Deep MANTA
- 8. Vehicle Detection from 3D Lidar Using Fully Convolutional Network解析(3D-CNN模型)
- 9. 【3D 目标检测】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
- 10. Joint 3D Proposal Generation and Object Detection from View Aggregation论文学习