Improved Multiview Decomposition for Single-Image High-Resolution 3D Object Reconstruction
المؤلفون المشاركون
Peng, Jiansheng
Fu, Kui
Wei, Qingjin
Qin, Yong
He, Qiwen
المصدر
Wireless Communications and Mobile Computing
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-28
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
As a representative technology of artificial intelligence, 3D reconstruction based on deep learning can be integrated into the edge computing framework to form an intelligent edge and then realize the intelligent processing of the edge.
Recently, high-resolution representation of 3D objects using multiview decomposition (MVD) architecture is a fast reconstruction method for generating objects with realistic details from a single RGB image.
The results of high-resolution 3D object reconstruction are related to two aspects.
On the one hand, a low-resolution reconstruction network represents a good 3D object from a single RGB image.
On the other hand, a high-resolution reconstruction network maximizes fine low-resolution 3D objects.
To improve these two aspects and further enhance the high-resolution reconstruction capabilities of the 3D object generation network, we study and improve the low-resolution 3D generation network and the depth map superresolution network.
Eventually, we get an improved multiview decomposition (IMVD) network.
First, we use a 2D image encoder with multifeature fusion (MFF) to enhance the feature extraction capability of the model.
Second, a 3D decoder using an effective subpixel convolutional neural network (3D ESPCN) improves the decoding speed in the decoding stage.
Moreover, we design a multiresidual dense block (MRDB) to optimize the depth map superresolution network, which allows the model to capture more object details and reduce the model parameters by approximately 25% when the number of network layers is doubled.
The experimental results show that the proposed IMVD is better than the original MVD in the 3D object superresolution experiment and the high-resolution 3D reconstruction experiment of a single image.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Peng, Jiansheng& Fu, Kui& Wei, Qingjin& Qin, Yong& He, Qiwen. 2020. Improved Multiview Decomposition for Single-Image High-Resolution 3D Object Reconstruction. Wireless Communications and Mobile Computing،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1214821
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Peng, Jiansheng…[et al.]. Improved Multiview Decomposition for Single-Image High-Resolution 3D Object Reconstruction. Wireless Communications and Mobile Computing No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1214821
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Peng, Jiansheng& Fu, Kui& Wei, Qingjin& Qin, Yong& He, Qiwen. Improved Multiview Decomposition for Single-Image High-Resolution 3D Object Reconstruction. Wireless Communications and Mobile Computing. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1214821
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1214821
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر