論文閱讀《Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching》
Cascade residual learning (兩階段) Problem: 不適定區域（目標遮擋、重複紋理、無紋理等）難以產生高質量的視差 貢獻： DispNet + up-conv 恢復到全分辨率輸出，使視差圖像包含更多細節 校正初始視差（多規模殘差學習機制提供更多有效的優化） Tip: 直接學習殘差信號比直接學習視差更容易 改進： 非一個個堆積殘差塊，殘差信號不能直接監督，用多
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相關文章
- 1. 論文閱讀《Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching》
- 2. 論文閱讀筆記《Cascade Residual Learning A Two-stage Convolutional Neural Network for Stereo Matching》
- 3. Computing the Stereo Matching Cost with a Convolutional Neural Network
- 4. 論文學習——Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
- 5. 論文閱讀筆記《Pyramid Stereo Matching Network》
- 6. 論文閱讀《Pyramid Stereo Matching Network》
- 7. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting論文閱讀
- 8. 《A Convolutional Neural Network Cascade for Face Detection》
- 9. 論文《Matching Networks for One Shot Learning》閱讀
- 10. 論文閱讀《EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection》