Learning Rich Features from RGB-D Images for Object Detection and Segmentation(ECCV2014)
论文：https://arxiv.org/abs/1407.5736 Abstract 在本文中，我们使用语义丰富的图像和深度特征研究RGB-D图像的物体检测问题。我们提出了一种新的地心嵌入geocentric embedding？用于深度图像，除了水平差异之外，还为每个像素编码高于地面的高度和重力角度。我们证明这种地心嵌入？比使用原始深度图像更好地学习使用卷积神经网络的特征表示。我们的最终物体检
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相关文章
- 1. Learning Region Features for Object Detection
- 2. 《Learning Rich Features at High-Speed for Single-Shot Object Detection》笔记
- 3. 【论文笔记】：Learning Rich Features at High-Speed for Single-Shot Object Detection
- 4. Learning Rich Features at High-Speed for Single-Shot Object Detection 论文笔记
- 5. 论文笔记：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初级翻译
- 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 ）