PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes—2017（筆記）
PoseCNN：用卷積神經網絡估計雜亂場景中目標6D姿態—2017（筆記） 文章提出了新的PoseCNN姿態估計網絡，通過CNN提取圖像特徵，然後分三路進行目標分割標籤標註、平移估計和姿態估計得到目標6D姿態，其中通過應用新型損失函數，能夠較好地估計對稱目標。 ----------------- Occlusion、symmetric object、only RGB 摘要 PoseCNN，這是一種
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相關文章
- 1. 論文筆記01——PoseCNN：A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
- 2. Learning latent geometric consistency for 6D object pose estimation in heavily cluttered scenes
- 3. 深度學習6D姿態估計pose estimation分類
- 4. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
- 5. [CVPR 2019]Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
- 6. SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again—2017（筆記）
- 7. 【paper】DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion 6D姿態估計
- 8. Recovering 6D Object Pose from RGB Indoor Image 閱讀筆記
- 9. Convolutional Neural Network - Object Detection
- 10. Segmentation-driven 6D Object Pose Estimation論文理解