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DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs [chapter]

Shi Yan, Chenglei Wu, Lizhen Wang, Feng Xu, Liang An, Kaiwen Guo, Yebin Liu
2018 Lecture Notes in Computer Science  
We propose a cascaded Depth Denoising and Refinement Network (DDRNet) to tackle this problem by leveraging the multi-frame fused geometry and the accompanying high quality color image through a joint training  ...  Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest Iphone X.  ...  Conclusion We presented the first end-to-end trainable network for depth map denoising and refinement for consumer depth cameras.  ... 
doi:10.1007/978-3-030-01249-6_10 fatcat:ru4ijo7novhj7gfkep5r326s4a

Edge-guided single facial depth map super-resolution using CNN

Fan Zhang, Na Liu, Liang Chang, Fuqing Duan, Xiaoming Deng
2021 IET Image Processing  
In recent years, consumer depth cameras have been widely used in digital entertainment and human-machine interaction due to the advantages of real-time performance and low cost.  ...  In this work, the authors propose an edge-guided convolutional neural network for single facial depth map super-resolution.  ...  Acknowledgments This work was supported by the National Key R&D Program of China under Grant No. 2019YFC1521103, the Natural Science Foundation of China, grant nos. 61572078, 61772050 and U19B2039.  ... 
doi:10.1049/iet-ipr.2019.1623 fatcat:mspahvcimnbwre6j7lmpidttdu