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A fusion network for semantic segmentation using RGB-D data
Author(s): Jiahui Yuan; Kun Zhang; Yifan Xia; Lin Qi; Junyu Dong
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Paper Abstract

Semantic scene parsing is considerable in many intelligent field, including perceptual robotics. For the past few years, pixel-wise prediction tasks like semantic segmentation with RGB images has been extensively studied and has reached very remarkable parsing levels, thanks to convolutional neural networks (CNNs) and large scene datasets. With the development of stereo cameras and RGBD sensors, it is expected that additional depth information will help improving accuracy. In this paper, we propose a semantic segmentation framework incorporating RGB and complementary depth information. Motivated by the success of fully convolutional networks (FCN) in semantic segmentation field, we design a fully convolutional networks consists of two branches which extract features from both RGB and depth data simultaneously and fuse them as the network goes deeper. Instead of aggregating multiple model, our goal is to utilize RGB data and depth data more effectively in a single model. We evaluate our approach on the NYU-Depth V2 dataset, which consists of 1449 cluttered indoor scenes, and achieve competitive results with the state-of-the-art methods.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061523 (10 April 2018); doi: 10.1117/12.2304501
Show Author Affiliations
Jiahui Yuan, Ocean Univ. of China (China)
Kun Zhang, Taian Tobacco Co., Ltd. (China)
Yifan Xia, Ocean Univ. of China (China)
Lin Qi, Ocean Univ. of China (China)
Junyu Dong, Ocean Univ. of China (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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