Learning Rich Features from RGB-D Images for Object Detection and Segmentation(ECCV2014)
論文：https://arxiv.org/abs/1407.5736 Abstract 在本文中，我們使用語義豐富的圖像和深度特徵研究RGB-D圖像的物體檢測問題。我們提出了一種新的地心嵌入geocentric embedding？用於深度圖像，除了水平差異之外，還爲每個像素編碼高於地面的高度和重力角度。我們證明這種地心嵌入？比使用原始深度圖像更好地學習使用卷積神經網絡的特徵表示。我們的最終物體檢
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相關文章
- 1. Learning Region Features for Object Detection
- 2. 《Learning Rich Features at High-Speed for Single-Shot Object Detection》筆記
- 3. 【論文筆記】：Learning Rich Features at High-Speed for Single-Shot Object Detection
- 4. Learning Rich Features at High-Speed for Single-Shot Object Detection 論文筆記
- 5. 論文筆記：Learning Region Features for Object Detection
- 6. Rich feature hierarchies for accurate object detection and semantic segmentation
- 7. Implicit 3D Orientation Learning for 6D Object Detection from RGB Images初級翻譯
- 8. Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
- 9. indoor segmentation and support inference from rgbd images
- 10. Object Detection Network———Rcnn（Rich feature hierarchies for accurate oject detection and semantic ）