5.Deeper Depth Prediction with Fully Convolutional Residual Networks
論文在這兒 大體介紹 爲了解決單目RGB圖像的深度估計問題，本篇論文提出了一個包含殘差學習的全卷積結構(fully convolutional architecture)，進行端到端訓練，並不需要進行像CRFs之類的後處理操作。爲了能夠提高輸出分辨率，在神經網絡中加入了上採樣(up-sampling)。同時採用能由深度圖中常見的值分佈進行驅動的reverse Huber loss進行優化。這個模型
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相關文章
- 1. Deeper Depth Prediction with Fully Convolutional Residual Networks
- 2. (論文閱讀)Deeper Depth Prediction with Fully Convolutional Residual Networks
- 3. 論文筆記_S2D.16-2016-3DV-Deeper Depth Prediction with Fully Convolutional Residual Networks
- 4. Deeper Depth Prediction with Fully Convolutional Residual Networks(FCRN)全卷積殘差網絡概述
- 5. Fast Image Processing with Fully-Convolutional Networks
- 6. Fully Convolutional Networks
- 7. Automatic skin lesion segmentation with fully convolutional-deconvolutional networks
- 8. 【DeepLearning】簡述Visual Tracking with Fully Convolutional Networks
- 9. FCN(Fully convolutional networks)閱讀
- 10. Fully Convolutional Adaptation Networks for Semantic Segmentation