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Real-Time 3D Reconstruction Method Based on Monocular Vision.
Jia, Qingyu; Chang, Liang; Qiang, Baohua; Zhang, Shihao; Xie, Wu; Yang, Xianyi; Sun, Yangchang; Yang, Minghao.
Afiliación
  • Jia Q; Guangxi Key Laboratory of Image and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China.
  • Chang L; Guangxi Key Laboratory of Image and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China.
  • Qiang B; Guangxi Key Laboratory of Image and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China.
  • Zhang S; Guangxi Key Laboratory of Image and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China.
  • Xie W; Guangxi Key Laboratory of Image and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China.
  • Yang X; Guangxi Key Laboratory of Image and Graphics Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China.
  • Sun Y; Research Center for Brain-Inspired Intelligence (BII), Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing 100190, China.
  • Yang M; Research Center for Brain-Inspired Intelligence (BII), Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing 100190, China.
Sensors (Basel) ; 21(17)2021 Sep 02.
Article en En | MEDLINE | ID: mdl-34502800
Real-time 3D reconstruction is one of the current popular research directions of computer vision, and it has become the core technology in the fields of virtual reality, industrialized automatic systems, and mobile robot path planning. Currently, there are three main problems in the real-time 3D reconstruction field. Firstly, it is expensive. It requires more varied sensors, so it is less convenient. Secondly, the reconstruction speed is slow, and the 3D model cannot be established accurately in real time. Thirdly, the reconstruction error is large, which cannot meet the requirements of scenes with accuracy. For this reason, we propose a real-time 3D reconstruction method based on monocular vision in this paper. Firstly, a single RGB-D camera is used to collect visual information in real time, and the YOLACT++ network is used to identify and segment the visual information to extract part of the important visual information. Secondly, we combine the three stages of depth recovery, depth optimization, and deep fusion to propose a three-dimensional position estimation method based on deep learning for joint coding of visual information. It can reduce the depth error caused by the depth measurement process, and the accurate 3D point values of the segmented image can be obtained directly. Finally, we propose a method based on the limited outlier adjustment of the cluster center distance to optimize the three-dimensional point values obtained above. It improves the real-time reconstruction accuracy and obtains the three-dimensional model of the object in real time. Experimental results show that this method only needs a single RGB-D camera, which is not only low cost and convenient to use, but also significantly improves the speed and accuracy of 3D reconstruction.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagenología Tridimensional / Realidad Virtual Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagenología Tridimensional / Realidad Virtual Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza