Unsupervised Learning of Stereo Matching
Cost-volume Computation 用對應的分支來計算cost-volume，輸入即左右圖像，生成cost-volume。這部分是由八個卷基層構成的雙塔結構。每個層後面有normalization和Relu層。這些層對兩個圖像的每個塊都會產生特徵向量。這些特徵向量再進入correlation layer，計算得到cost-volume，這個correlation層就是用的Dispnet
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相關文章
- 1. Self-Supervised Learning for Stereo Matching with Self-Improving Ability
- 2. 《Efficient Deep Learning for Stereo Matching》學習筆記
- 3. Pyramid Stereo Matching Network
- 4. 【論文筆記-Stereo】Pyramid Stereo Matching Network
- 5. 論文《Patchmatch-Based Robust Stereo Matching Patchmatch-Based Robust Stereo Matching》學習
- 6. Deep Temporal Clustering: Fully unsupervised learning of time-domain features
- 7. GoodPoint: unsupervised learning of keypoint detection and description∗
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- 9. [解讀] Deep Clustering for Unsupervised Learning of Visual Features
- 10. Dynamic Label Graph Matching for Unsupervised Video Re-Identification