論文筆記：ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape
1introduction 這篇cvpr2019文章提出了一種度量精確的單目3D目標檢測端對端方法。（arxiv:1812.02781） 主要貢獻有三點： 一種度量精確的單目3D目標檢測端對端方法，包括一種可微分的2D ROI到3D ROI 提升映射，並提供了用於迴歸3D box 實例的所有組件； 一種用於在度量空間對其3D box的損失函數，直接優化其關於真值的誤差； 擴展模型，將其用於預測度量
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- 1. SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again—2017（筆記）
- 2. Unsupervised Learning of Shape and Pose with Differentiable Point Clouds && NIPS 2018 論文筆記
- 3. 【3D 目標檢測】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
- 4. From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks && 2020論文筆記
- 5. (3D-HPE)End-to-end Recovery of Human Shape and Pose
- 6. Beyond Pixels Leveraging Geometry and Shape Cues for Online[ICRA18]閱讀筆記
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相關文章
- 1. SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again—2017（筆記）
- 2. Unsupervised Learning of Shape and Pose with Differentiable Point Clouds && NIPS 2018 論文筆記
- 3. 【3D 目標檢測】2019 CVPR Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
- 4. From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks && 2020論文筆記
- 5. (3D-HPE)End-to-end Recovery of Human Shape and Pose
- 6. Beyond Pixels Leveraging Geometry and Shape Cues for Online[ICRA18]閱讀筆記
- 7. 機器人視覺抓取論文及代碼資源
- 8. Fast single shot detection and pose estimation 論文筆記
- 9. Segmentation-driven 6D Object Pose Estimation論文理解
- 10. 論文學習筆記：《DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image》