Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution

• URL: http://arxiv.org/abs/2002.05067v3
• Date: Fri, 12 Jun 2020 04:10:34 GMT
• Title: Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution
• Authors: Chuhua Xian, Dongjiu Zhang, Chengkai Dai, Charlie C. L. Wang
• Abstract summary: We propose a deep-learning based approach to efficiently generate RGB-D images with completed information in high resolution. As an end-to-end approach, high fidelity RGB-D images can be generated efficiently at the rate of around 21 frames per second.
• Score: 10.085742605397124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using the raw data from consumer-level RGB-D cameras as input, we propose a deep-learning based approach to efficiently generate RGB-D images with completed information in high resolution. To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules -- the completion module, the refinement module and the super-resolution module. The completion module is based on an architecture of encoder-decoder, where the features of input raw RGB-D will be automatically extracted by the encoding layers of a deep neural-network. The decoding layers are applied to reconstruct the completed depth map, which is followed by a refinement module to sharpen the boundary of different regions. For the super-resolution module, we generate RGB-D images in high resolution by multiple layers for feature extraction and a layer for up-sampling. Benefited from the adaptive convolution operators newly proposed in this paper, our results outperform the existing deep-learning based approaches for RGB-D image complete and super-resolution. As an end-to-end approach, high fidelity RGB-D images can be generated efficiently at the rate of around 21 frames per second.

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