Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Archit
代码开源：http://cs.nyu.edu/~deigen/dnl/ 摘要中说，这篇文章是一石三鸟，深度预测，表面估计和语义分析，同样的网络结构的三个应用。。。 网络结构 这个算法结构比较有意思，首先根据整张图片进行粗提取一个全局的输出预测，整个结构是取自于[8]，不过创新就是1，更深了，2，又加了一个尺度，输出尺寸是输入的一半。3.多通道特征，而不是将特征由1传递到2（是因为concat嘛？有
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相关文章
- 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... 论文笔记
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- 7. 解读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
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- 10. 2015-CVPR-Direction Matters_ Depth Estimation with a Surface Normal Classifier