High Quality Depth Estimation from Monocular Images Based on Depth Prediction and Enhancement Sub-Networks

IEEE International Conference on Multimedia and Expo (ICME), 2018

Recommended citation: X. Duan, X. Ye, Y. Li and H. Li, "High Quality Depth Estimation from Monocular Images Based on Depth Prediction and Enhancement Sub-Networks," 2018 IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA, 2018, pp. 1-6, doi: 10.1109/ICME.2018.8486539. https://ieeexplore.ieee.org/document/8486539

(ChatGPT-Generated) The paper proposes a novel depth estimation framework based on deep convolutional neural network (CNN) to learn the mapping between monocular images and depth maps, which includes a depth prediction network based on the ResNet architecture to infer the scene depth from color image and a depth enhancement network concatenated to the end of the depth prediction network to obtain a high resolution depth map, and experimental results show that the proposed method outperforms other methods on benchmark RGB-D datasets and achieves state-of-the-art performance.

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Recommended citation: X. Duan, X. Ye, Y. Li and H. Li, “High Quality Depth Estimation from Monocular Images Based on Depth Prediction and Enhancement Sub-Networks,” 2018 IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA, 2018, pp. 1-6, doi: 10.1109/ICME.2018.8486539.