Color-guided depth map super resolution using joint convolutional neural network

Ziyue Zhang; Weiqi Jin; Yingjie Li

doi:10.1117/12.2580112

5 November 2020 Color-guided depth map super resolution using joint convolutional neural network

Ziyue Zhang, Weiqi Jin, Yingjie Li

Proceedings Volume 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics; 115650Z (2020) https://doi.org/10.1117/12.2580112
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

As an expression of three-dimensional information, depth map provides more possibilities for many computer vision applications, which puts forward higher quality requirements for depth maps. However, the spatial resolution of depth map is always low, which limits its potential. In this paper, we present a depth map super-resolution based on joint convolutional neural network (J-CNN). The network combines RGB image to guide the reconstruction of low-resolution depth map. Our model consists of three subnets. Subnet 1 uses simple convolutional layers to extract rough features of depth maps. Subnet 2 and 3 use a progressive up and progressive down sampling network structure. This structure can greatly expand the receptive fields and help to extract more fine features of the images at different scales. We use it to extract the complex features in the depth map and RGB image. Finally, convolutional layers connect three subnets to transfer useful information from RGB image to depth map. The final up-sampling reconstructed operation is realized by sub-pixel convolution, effectively avoiding the “checkerboard effect”. Our J-CNN is evaluated on Middlebury datasets which shows improved performance compared with six advanced methods. Our model also shows strong robustness under large sampling factor (16 times).

Citation Download Citation

Ziyue Zhang, Weiqi Jin, and Yingjie Li "Color-guided depth map super resolution using joint convolutional neural network", Proc. SPIE 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics, 115650Z (5 November 2020); https://doi.org/10.1117/12.2580112

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