每天一篇論文 291/365 DSNet: Joint Learning for Scene Segmentation and Disparity Estimation
原文 https://mp.weixin.qq.com/s/Uter5evHzR9Wgrdwb4zVRw 摘要 近年來，研究者們嘗試了場景語義的聯合預測和光流估計，證明了兩種任務之間的相互改進。此外，深度信息對於場景理解也是必不可少的，而視差估計對於密集深度圖的輸出也是必不可少的。這種任務與光流估計有很大的相似性，因爲它們都可以轉化爲在兩個圖像幀的一個位置捕獲差異的問題。然而，據我們所知，目前用於
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相關文章
- 1. 論文筆記_2018-ECCV-Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
- 2. 每天一篇論文 306/365Self-supervised Learning for Single View Depth and Surface Normal Estimation
- 3. FADNet:A Fast and Accurate Network for Disparity Estimation的論文翻譯
- 4. Segmentation簡記-Joint shape learning and segmentation for medical images
- 5. 論文閱讀：Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- 6. 論文筆記_2013-CVPR-Joint 3D Scene Reconstruction and Class Segmentation
- 7. 每天一篇論文298/365 Multi-Task Template Matching for Object Detection, Segmentation and Pose
- 8. #每天一篇論文 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. 論文閱讀筆記《Learning for Disparity Estimation through Feature Constancy》