NDLI: ResFusion: deeply fused scene parsing network for RGB-D images

Content Provider	IET Digital Library
Author	Dai, Juting Tang, Xinyi
Abstract	Scene parsing is a very challenging work for complex and diverse scenes. In this study, the authors address the problem of semantic segmentation of indoor scenes for red, green, blue-depth (RGB-D) images. Most existing works use only the colour or photometric information for this problem. Here, they present an approach to fusing feature maps between colour network branch and depth network branch to integrate the photometric information and geometric information, which improves the semantic segmentation performance. They propose a novel convolutional neural network that uses ResNet as a baseline network. Their proposed network adopts a spatial pyramid pooling module to make full use of different sub-region representations. Their proposed network also adopts multiple feature maps fusion modules to integrate texture and structure information between the colour branch and depth branch. Moreover, their proposed network has multiple auxiliary loss branches together with the main loss function to prevent the gradient of frontal layers disappear and accelerate the training phase of the fusion part. Comprehensive experimental evaluations show that their proposed network ‘ResFusion’ improves the performance greatly over the baseline network and has achieved competitive performance compared with other state-of-the-art methods on the challenging SUN RGB-D benchmark.
Starting Page	1171
Ending Page	1178
Page Count	8
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 8, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5218
Journal	IET Computer Vision
Publisher Date	2018-07-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Colour Information Colour Network Branch Computer Vision And Image Processing Technique Convolutional Neural Network Depth Network Branch Feedforward Neural Network Geometric Information Image Colour Analysis Image Fusion Image Segmentation Image Texture Indoor Scenes Multiple Feature Maps Fusion Module Neural Computing Technique Optical, Image And Video Signal Processing Photometric Information Red-green-blue-depth Image ResFusion ResNet RGB-D Image Scene Parsing Network Semantic Segmentation Performance Semantic Segmentation Problem Sensor Fusion Spatial Pyramid Pooling Module Structure Information Subregion Representations Texture Information
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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