基于RGB-D图像的语义场景补全研究进展综述
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袁夏,E-mail:yuanxia@njust.edu.cn

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国家自然科学基金面上项目(61773210)


Research on Semantic Scene Completion Based on RGB-D Images
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    摘要:

    近年来随着计算机视觉领域的不断发展,三维场景的语义分割和形状补全受到学术界和工业界的广泛关注.其中,语义场景补全是这一领域的新兴研究,该研究以同时预测三维场景的空间布局和语义标签为目标,在近几年得到快速发展.本文对近些年该领域提出的基于RGB-D图像的方法进行了分类和总结.根据有无使用深度学习将语义场景补全方法划分为传统方法和基于深度学习的方法两大类.其中,对于基于深度学习的方法,根据输入数据类型将其划分为基于单一深度图像的方法和基于彩色图像联合深度图像的方法.在对已有方法分类和概述的基础上,本文对语义场景补全任务所使用的相关数据集进行了整理,并分析了现有方法的实验结果.最后,本文总结了该领域面临的挑战和发展前景.

    Abstract:

    In recent years, with the continuous development of computer vision, semantic segmentation and shape completion of 3D scene have been paid more and more attention by academia and industry. Among them, semantic scene completion is an emerging research in this field, which aims to to simultaneously predict the spatial layout and semantic labels of a 3D scene, and has developed rapidly in recent years. In this paper, we classify and summarize the methods based on RGB-D images proposed in this field in few years. These methods are divided into two categories based on whether deep learning is used or not, which include traditional methods and deep learning-based methods. Among them, the methods based on deep learning are divided into two categories according to the input data type, which are the methods based on single depth image and the methods based on RGB-D images. Based on the classification and overview of the existing methods, we collate the relevant datasets used for semantic scene completion task and analyze the experimental results. Finally, we summarize the challenges and development prospects of this field.

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张康,安泊舟,李捷,袁夏,赵春霞.基于RGB-D图像的语义场景补全研究进展综述.软件学报,,():0

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  • 收稿日期:2020-09-16
  • 最后修改日期:2021-05-31
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  • 在线发布日期: 2021-10-20
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