End-to-End Learning of Geometry and Context for Deep Stereo Regression
代码Keras: https://github.com/LinHungShi/GCNetwork 摘： 使用深度特征表示组成成本向量，充分利用3D卷积提取上下文的纹理信息。深度值以差分softargmin操作相规范化，没有额外的预处理或者正则化得到一种端到端的方法 1.介： 立体匹配算法应该看重全局，而非局部。比如车窗，如果以整辆车的视角去看，那么就不怎么会出现问题，而如果以局部车窗去看这个物体，
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相关文章
- 1. 论文阅读笔记《End-to-End Learning of Geometry and Context for Deep Stereo Regression》
- 2. 3D Bounding Box Estimation Using Deep Learning and Geometry
- 3. Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca D
- 4. 《Efficient Deep Learning for Stereo Matching》学习笔记
- 5. Geometry Meets Deep Learning(专栏)
- 6. Audio alaysis and Deep Learning
- 7. Deep Learning Models and Code for Pose Estimation
- 8. Inspirational Applications of Deep Learning
- 9. CVPR2017: Learning Deep Context-aware Features over Body and Latent Parts for
- 10. Unsupervised Learning of Stereo Matching