Computing the Stereo Matching Cost with a Convolutional Neural Network
譯： 摘要： 從矯正的圖像對中提取深度信息，訓練CNN預測兩圖如何匹配並且計算立體匹配損耗。成本由交叉成本聚合和半球匹配所改善。 1.介紹： 考慮如下問題：兩張從不同位置的相機所攝的圖像，目標是計算左圖每個像素的差異。差異指的是左右兩圖的水平位置差異d，其中的差異可由深度z（即物體到相機的距離）（公式未知），上述問題變成了立體重建的子問題，即可從一張或多張圖片中重建出3D的形狀出來，一個典型的立體
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相關文章
- 1. 【翻譯】Computing the Stereo Matching Cost with a Convolutional Neural Network
- 2. Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
- 3. 論文學習——Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
- 4. 【翻譯學習筆記】Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
- 5. Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches 譯文
- 6. 論文閱讀《Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching》
- 7. Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture
- 8. Pyramid Stereo Matching Network
- 9. Logistic-Regression-with-a-Neural-Network-mindset
- 10. 論文閱讀筆記《Cascade Residual Learning A Two-stage Convolutional Neural Network for Stereo Matching》