research-article

PGDENet: Progressive Guided Fusion and Depth Enhancement Network for RGB-D Indoor Scene Parsing

Authors:

Lu YuAuthors Info & Claims

IEEE Transactions on Multimedia, Volume 25

Pages 3483 - 3494

https://doi.org/10.1109/TMM.2022.3161852

Published: 01 January 2023 Publication History

Abstract

Scene parsing is a fundamental task in computer vision. Various RGB-D (color and depth) scene parsing methods based on fully convolutional networks have achieved excellent performance. However, color and depth information are different in nature and existing methods cannot optimize the cooperation of high-level and low-level information when aggregating modal information, which introduces noise or loss of key information in the aggregated features and generates inaccurate segmentation maps. The features extracted from the depth branch are weak because of the low quality of the depth map, which results in unsatisfactory feature representation. To address these drawbacks, we propose a progressive guided fusion and depth enhancement network (PGDENet) for RGB-D indoor scene parsing. First, high-quality RGB images are used to improve depth data through a depth enhancement module, in which the depth maps are strengthened in terms of channel and spatial correlations. Then, we integrate information from the RGB and enhance depth modalities using a progressive complementary fusion module, in which we start with high-level semantic information and move down layerwise to guide the fusion of adjacent layers while reducing hierarchy-based differences. Extensive experiments are conducted on two public indoor scene datasets, and the results show that the proposed PGDENet outperforms state-of-the-art methods in RGB-D scene parsing.

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cover image IEEE Transactions on Multimedia

IEEE Transactions on Multimedia Volume 25, Issue

2023

8932 pages

ISSN:1520-9210

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Published: 01 January 2023

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Cited By

Zhao ZLi JWang LWang YLu HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)MaskMentor: Unlocking the Potential of Masked Self-Teaching for Missing Modality RGB-D Semantic SegmentationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681698(1915-1923)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681698
Hao ZXiao ZLuo YGuo JWang JShen LHu HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)PrimKD: Primary Modality Guided Multimodal Fusion for RGB-D Semantic SegmentationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681253(1943-1951)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681253
Xie BDeng YShao ZLi Y(2024)EISNet: A Multi-Modal Fusion Network for Semantic Segmentation With Events and ImagesIEEE Transactions on Multimedia10.1109/TMM.2024.338025526(8639-8650)Online publication date: 21-Mar-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3380255
Dong SZhou WXu CYan W(2024)EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene ParsingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330636825:1(657-669)Online publication date: 1-Jan-2024
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Zhou WJian BFang MDong XLiu YJiang Q(2024)DGPINet-KD: Deep Guided and Progressive Integration Network With Knowledge Distillation for RGB-D Indoor Scene AnalysisIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.338235434:9(7844-7855)Online publication date: 1-Sep-2024
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