FlowNet 2.0 Evolution of Optical Flow Estimation with Deep Networks
Abstract FlowNet算法證明了光流計算可以被轉換爲學習問題。在小位移以及現實場景之下，flownet還無法與傳統方法比較。 本文提出了3點改進：在訓練的不同階段使用不同的訓練集是重要的。第二：提出了warp操作，使用中間預測出來的粗光流應用於第二張圖片之上。第三：使用一個子模塊專門處理小位移。 Introduction 由Dosovitskiy et al.（A. Dosovitski
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相關文章
- 1. FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
- 2. FlowNet2.0:Evolution of Optical Flow Estimation with Deep Networks
- 3. CNN光流計算2--FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
- 4. 論文翻譯：FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
- 5. FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks 代碼及環境配置
- 6. FlowNet: Learning Optical Flow with Convolutional Networks
- 7. [翻譯]FlowNet: Learning Optical Flow with Convolutional Networks
- 8. Optical Flow 發展歷程 (1)
- 9. 【視頻檢測】FlowNet: Learning Optical Flow with Convolutional Networks
- 10. CNN光流計算--FlowNet: Learning Optical Flow with Convolutional Networks