Learning latent geometric consistency for 6D object pose estimation in heavily cluttered scenes
Learning latent geometric consistency for 6D object pose estimation in heavily cluttered scenes 在雜亂無章的場景中學習6D對象姿態估計的潛在幾何一致性 abstract 從RGB-D圖像估計6D對象姿態（3D旋轉和平移）是計算機視覺中一項重要且具有挑戰性的任務，已廣泛應用於各種應用中，例如機器人操縱，自
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- 1. PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes—2017（筆記）
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相關文章
- 1. PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes—2017（筆記）
- 2. 論文筆記01——PoseCNN：A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
- 3. 深度學習6D姿態估計pose estimation分類
- 4. [CVPR 2019]Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
- 5. Segmentation-driven 6D Object Pose Estimation論文理解
- 6. 2020.10.03讀 Robust 6D Object Pose Estimation by Learning RGB_D Features
- 7. 【paper】DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion 6D姿態估計
- 8. Single-Stage 6D Object Pose Estimation——6D姿態估計
- 9. Deep Learning Models and Code for Pose Estimation
- 10. 【6Dof位姿估計】Multi-path Learning for Object Pose Estimation Across Domains論文理解