Digging Into Self-Supervised Monocular Depth Estimation（2019.8）
深刻研究基於自監督的單目深度估計 貢獻： （1）一個最小投影損失來解決遮擋問題。網絡 （2）全分辨率的多尺度採樣方法，減小視覺混亂app （3）設計一個 auto-masking loss 來忽略相機運動假設函數 1、介紹 做爲監督學習的替代選擇，自監督方法被證實可以僅使用合成的立體圖像對或單目視頻對單目深度估計模型進行訓練。學習 在兩種自監督方法中，單目視頻比基於立體圖像的監
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相關文章
- 1. 11.Unsupervised Monocular Depth Estimation with Left-Right Consistency
- 2. Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics 之論文詳解
- 3. 每天一篇論文 301/365 3D Packing for Self-Supervised Monocular Depth Estimation
- 4. unsupervised learning of monocular depth estimation with bundle adjustment, Super-Resolution 論文
- 5. 論文筆記-Deep Ordinal Regression Network for Monocular Depth Estimation
- 6. Unsupervised Monocular Depth Estimation with Left-Right Consistency-CVPR17學習筆記
- 7. Unsupervised Monocular Depth Estimation With Left-Right Consistency 論文筆記
- 8. CVPR2017 Oral【論文筆記】Unsupervised Monocular Depth Estimation with Left-Right Consistency
- 9. 每天一篇論文 SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation
- 10. Real-Time monocular depth estimation using synthetic data with domain adaptation via IST