每天一篇论文 291/365 DSNet: Joint Learning for Scene Segmentation and Disparity Estimation
原文 https://mp.weixin.qq.com/s/Uter5evHzR9Wgrdwb4zVRw 摘要 近年来，研究者们尝试了场景语义的联合预测和光流估计，证明了两种任务之间的相互改进。此外，深度信息对于场景理解也是必不可少的，而视差估计对于密集深度图的输出也是必不可少的。这种任务与光流估计有很大的相似性，因为它们都可以转化为在两个图像帧的一个位置捕获差异的问题。然而，据我们所知，目前用于
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相关文章
- 1. 论文笔记_2018-ECCV-Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
- 2. 每天一篇论文 306/365Self-supervised Learning for Single View Depth and Surface Normal Estimation
- 3. FADNet:A Fast and Accurate Network for Disparity Estimation的论文翻译
- 4. Segmentation简记-Joint shape learning and segmentation for medical images
- 5. 论文阅读：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- 6. 论文笔记_2013-CVPR-Joint 3D Scene Reconstruction and Class Segmentation
- 7. 每天一篇论文298/365 Multi-Task Template Matching for Object Detection, Segmentation and Pose
- 8. #每天一篇论文 261/365 PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds
- 9. What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
- 10. 论文阅读笔记《Learning for Disparity Estimation through Feature Constancy》