End-to-End Learning of Geometry and Context for Deep Stereo Regression
代碼Keras: https://github.com/LinHungShi/GCNetwork 摘： 使用深度特徵表示組成成本向量，充分利用3D卷積提取上下文的紋理信息。深度值以差分softargmin操作相規範化，沒有額外的預處理或者正則化得到一種端到端的方法 1.介： 立體匹配算法應該看重全局，而非局部。比如車窗，如果以整輛車的視角去看，那麼就不怎麼會出現問題，而如果以局部車窗去看這個物體，
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相關文章
- 1. 論文閱讀筆記《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》學習筆記
- 5. Geometry Meets Deep Learning(專欄)
- 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