Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Archit
代碼開源：http://cs.nyu.edu/~deigen/dnl/ 摘要中說，這篇文章是一石三鳥，深度預測，表面估計和語義分析，同樣的網絡結構的三個應用。。。 網絡結構 這個算法結構比較有意思，首先根據整張圖片進行粗提取一個全局的輸出預測，整個結構是取自於[8]，不過創新就是1，更深了，2，又加了一個尺度，輸出尺寸是輸入的一半。3.多通道特徵，而不是將特徵由1傳遞到2（是因爲concat嘛？有
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相關文章
- 1. 00040-Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional
- 2. Predicting Depth,Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Archite
- 3. Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale... 論文筆記
- 4. 論文筆記-深度估計(3)Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale...
- 5. Depth Map Prediction from a Single Image using a Multi-Scale Deep Network (2014 NIPS)
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- 7. 解讀Towards Unified Depth and Semantic Prediction from a Single Image（2）
- 8. Converting a DICOM image to a common graphic format and vice versa with DCMTK and CxImage
- 9. DeepLab-v2：Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully C
- 10. 2015-CVPR-Direction Matters_ Depth Estimation with a Surface Normal Classifier