ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems

时间 2021-01-17

标签 stereo disparity 繁體版

原文原文链接

Network architecture Loss function 主要用的左图与reconstructioned的左图之间的差异，叫做photometric loss，然后亮处像素之间的差异一定会比暗的地方的差异大的。所以像素之间的residual是跟像素的亮度有关。然而，亮度与视差实际上是不相关的。所以，需要消除亮度的影响。因为非监督里面，的确loss是像素之间的loss。 Weighte

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1. 论文阅读《ActiveStereoNet:End-to-End Self-Supervised Learning for Active Stereo Systems》
2. 【Active Learning - 03】Adaptive Active Learning for Image Classification
3. 【Active Learning - 05】Adversarial Sampling for Active Learning
4. Wide & Deep Learning for Recommender Systems
5. Paper Reading:Wide & Deep Learning for Recommender Systems
6. Learning Tree-based DeepModel for Recommender Systems
7. Collaborative Deep Learning for Recommender Systems
8. Wide &Deep Learning for Recommender Systems
9. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 论文笔记
10. Learning Parallax Attention for Stereo Image Super-Resolution

>>更多相关文章<<