FlowNet 2.0 Evolution of Optical Flow Estimation with Deep Networks
Abstract FlowNet算法证明了光流计算可以被转换为学习问题。在小位移以及现实场景之下，flownet还无法与传统方法比较。 本文提出了3点改进：在训练的不同阶段使用不同的训练集是重要的。第二：提出了warp操作，使用中间预测出来的粗光流应用于第二张图片之上。第三：使用一个子模块专门处理小位移。 Introduction 由Dosovitskiy et al.（A. Dosovitski
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相关文章
- 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光流计算2--FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
- 4. 论文翻译：FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
- 5. FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks 代码及环境配置
- 6. FlowNet: Learning Optical Flow with Convolutional Networks
- 7. [翻译]FlowNet: Learning Optical Flow with Convolutional Networks
- 8. Optical Flow 发展历程 (1)
- 9. 【视频检测】FlowNet: Learning Optical Flow with Convolutional Networks
- 10. CNN光流计算--FlowNet: Learning Optical Flow with Convolutional Networks