Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model

By Jingyu Yang, Xinchen Ye, Kun Li, Chunping Hou, Yao Wang. IEEE Transactions on Image Processing, 2014

307 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/6e94caaaa116b834221e025c186da8aa9d93b408

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chen Change Loy
2. Luc Van Gool
3. Xiaoou Tang
4. Xiaogang Wang
5. Peng Wang
6. Ming-Hsuan Yang
7. Alan L. Yuille
8. Dacheng Tao

In Media Technology

1. Chen Change Loy
2. Xiaoou Tang
3. Ruigang Yang
4. Peng Wang
5. Xinjing Cheng
6. Tak-Wai Hui
7. Chao Dong
8. Radu Timofte

Citations in the corpus

• Pyramid-Structured Depth MAP Super-Resolution Based on Deep Dense-Residual Network (2019, unknown): “AR [22], JID [23], SCN [24], MS [10], Lap [13] and MFR [11].”
• Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network (2018, background): “Recently, deep learning based enhancement yields impressive results on super resolution of both images [33,34] and depths [35,36,37,38].”
• Depth Map Super-Resolution by Deep Multi-Scale Guidance (2016, methodology): “We do not explicitly formulate an optimization problem as the global methods [2, 4, 5, 15] or design a fixed filter as the local methods [6, 13, 14] because CNN can be trained to address the upsampling problem.”
• Learning Depth with Convolutional Spatial Propagation Network (2018, background): “Recently, deep learning based enhancement yields impressive results on super resolution of both images [61], [62] and depths [63], [64], [65], [66].”