Structure Flow-Guided Network for Real Depth Super-Resolution Article Swipe

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Jiayi Yuan , Haobo Jiang , Xiang Li , Jianjun Qian , Jun Li , Jian Yang ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2301.13416

Real depth super-resolution (DSR), unlike synthetic settings, is a challenging task due to the structural distortion and the edge noise caused by the natural degradation in real-world low-resolution (LR) depth maps. These defeats result in significant structure inconsistency between the depth map and the RGB guidance, which potentially confuses the RGB-structure guidance and thereby degrades the DSR quality. In this paper, we propose a novel structure flow-guided DSR framework, where a cross-modality flow map is learned to guide the RGB-structure information transferring for precise depth upsampling. Specifically, our framework consists of a cross-modality flow-guided upsampling network (CFUNet) and a flow-enhanced pyramid edge attention network (PEANet). CFUNet contains a trilateral self-attention module combining both the geometric and semantic correlations for reliable cross-modality flow learning. Then, the learned flow maps are combined with the grid-sampling mechanism for coarse high-resolution (HR) depth prediction. PEANet targets at integrating the learned flow map as the edge attention into a pyramid network to hierarchically learn the edge-focused guidance feature for depth edge refinement. Extensive experiments on real and synthetic DSR datasets verify that our approach achieves excellent performance compared to state-of-the-art methods.

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Artificial Intelligence

Computer Science

Computer Vision

Enhanced Data Rates For Gsm Evolution

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Upsampling Artificial intelligence Computer science Pyramid (geometry) RGB color model Computer vision Depth map Enhanced Data Rates for GSM Evolution Grid Feature (linguistics) Flow (mathematics) Distortion (music) Modality (human–computer interaction) Image (mathematics) Mathematics Geometry Bandwidth (computing) Computer network Linguistics Philosophy Amplifier

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2301.13416
PDF: https://arxiv.org/pdf/2301.13416
OA Status: green
Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2301.13416

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Title: Structure Flow-Guided Network for Real Depth Super-Resolution

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-01-31

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Authors: Jiayi Yuan, Haobo Jiang, Xiang Li, Jianjun Qian, Jun Li, Jian Yang

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Landing page: https://arxiv.org/abs/2301.13416

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PDF URL: https://arxiv.org/pdf/2301.13416

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Concepts: Upsampling, Artificial intelligence, Computer science, Pyramid (geometry), RGB color model, Computer vision, Depth map, Enhanced Data Rates for GSM Evolution, Grid, Feature (linguistics), Flow (mathematics), Distortion (music), Modality (human–computer interaction), Image (mathematics), Mathematics, Geometry, Bandwidth (computing), Computer network, Linguistics, Philosophy, Amplifier

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Cited by: 1

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abstract_inverted_index.settings,	6
abstract_inverted_index.structure	36, 65
abstract_inverted_index.synthetic	5, 172
abstract_inverted_index.distortion	15
abstract_inverted_index.framework,	68
abstract_inverted_index.real-world	26
abstract_inverted_index.structural	14
abstract_inverted_index.trilateral	108
abstract_inverted_index.upsampling	94
abstract_inverted_index.challenging	9
abstract_inverted_index.degradation	24
abstract_inverted_index.experiments	168
abstract_inverted_index.flow-guided	66, 93
abstract_inverted_index.information	80
abstract_inverted_index.integrating	143
abstract_inverted_index.performance	181
abstract_inverted_index.potentially	47
abstract_inverted_index.prediction.	139
abstract_inverted_index.refinement.	166
abstract_inverted_index.significant	35
abstract_inverted_index.upsampling.	85
abstract_inverted_index.correlations	117
abstract_inverted_index.edge-focused	160
abstract_inverted_index.transferring	81
abstract_inverted_index.RGB-structure	50, 79
abstract_inverted_index.Specifically,	86
abstract_inverted_index.flow-enhanced	99
abstract_inverted_index.grid-sampling	132
abstract_inverted_index.inconsistency	37
abstract_inverted_index.cross-modality	71, 92, 120
abstract_inverted_index.hierarchically	157
abstract_inverted_index.low-resolution	27
abstract_inverted_index.self-attention	109
abstract_inverted_index.high-resolution	136
abstract_inverted_index.state-of-the-art	184
abstract_inverted_index.super-resolution	2
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	6
citation_normalized_percentile