What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
引:stereo傷不起，樣本要自己造，怎麼才能做出有效的逼真的讓模型很牛的樣本呢？ 對於分開的數據集分階段的訓練要比光訓練一個或者混起來一起訓練效果要好 通過特別複雜的光線使得數據集看起來更加的現實。這樣效果並不是很好，除非測試數據也是超現實光照~ 在訓練的時候模擬該相機的缺點，將會提升網絡測試該相機中圖像的性能。 以上就是所有的數據，可見虛擬數據多，真實數據少 以上就是這些數據的樣子，一般用的比
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相關文章
- 1. What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
- 2. What are some good books/papers for learning deep learning?
- 3. ProFlow: Learning to Predict Optical Flow
- 4. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 5. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
- 6. FlowNet: Learning Optical Flow with Convolutional Networks
- 7. FlowNet 2.0 Evolution of Optical Flow Estimation with Deep Networks
- 8. 論文解讀2-Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
- 9. 人羣計數：SFCN--Learning from Synthetic Data for Crowd Counting in the Wild
- 10. Game Development Theory 1：What makes a game ‘good’?