What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
引:stereo伤不起，样本要自己造，怎么才能做出有效的逼真的让模型很牛的样本呢？ 对于分开的数据集分阶段的训练要比光训练一个或者混起来一起训练效果要好 通过特别复杂的光线使得数据集看起来更加的现实。这样效果并不是很好，除非测试数据也是超现实光照~ 在训练的时候模拟该相机的缺点，将会提升网络测试该相机中图像的性能。 以上就是所有的数据，可见虚拟数据多，真实数据少 以上就是这些数据的样子，一般用的比
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相关文章
- 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. 论文解读2-Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
- 9. 人群计数：SFCN--Learning from Synthetic Data for Crowd Counting in the Wild
- 10. Game Development Theory 1：What makes a game ‘good’?