Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss

時間 2020-12-26

標籤 3d bouding box 简体版

原文原文鏈接

Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss insights 3DIou loss Homoscedastic noise Heteroscedastic noise 3D框的representation 重要結論 distance 越遠其va

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1. Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
2. Object-Detection-Loss
3. Improved Object Categorization and Detection Using Comparative Object Similarity
4. 【3D 目標檢測】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
5. 3D Object Detection 3D目標檢測綜述
6. Localization and Object Detection
7. Focal Loss for Dense Object Detection
8. One-shot object detection
9. 論文閱讀：EfficientDet: Scalable and Efficient Object Detection
10. 3D Bounding Box Estimation Using Deep Learning and Geometry
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1. Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
2. Object-Detection-Loss
3. Improved Object Categorization and Detection Using Comparative Object Similarity
4. 【3D 目標檢測】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
5. 3D Object Detection 3D目標檢測綜述
6. Localization and Object Detection
7. Focal Loss for Dense Object Detection
8. One-shot object detection
9. 論文閱讀：EfficientDet: Scalable and Efficient Object Detection
10. 3D Bounding Box Estimation Using Deep Learning and Geometry

>>更多相關文章<<