車輛檢測「Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monoc」
Deep Many Task，同時進行車輛檢測，部件定位，可視化特徵描述及3D維度估計。基於coarse-to-fine的目標proposal結構提升檢測性能。Deep MANTA可以定位不可見的車輛部位。 應用 3D車輛定位和方向估計可用於估計車輛速度和方向。 論文第一個貢獻是使用車輛特徵點編碼3D車輛信息，車輛是剛性的，可通過迴歸的方法預測隱藏的部分。結合3D數據集，將3D點投影到2D圖像中的
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相關文章
- 1. 車輛2D/3D--Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis
- 2. 論文閱讀：Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis
- 3. 3D車輛檢測 | Deep MANTA 算法筆記
- 4. 車輛檢測--A Closer Look at Faster R-CNN for Vehicle Detection
- 5. 車輛檢索「LEARNING A REPRESSION NETWORK FOR PRECISE VEHICLE SEARCH」
- 6. 3d fully convolutional network for vehicle detection in point cloud
- 7. 論文：Deep MANTA
- 8. Vehicle Detection from 3D Lidar Using Fully Convolutional Network解析(3D-CNN模型)
- 9. 【3D 目標檢測】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論文學習