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Multi-granularity relationship reasoning network for high-fidelity 3D shape reconstruction

文献类型：期刊论文

作者Li, Lei1,2; Zhou, Zhiyuan1,3; Wu, Suping1; Li, Pan1; Zhang, Boyang1,4
刊名PATTERN RECOGNITION
出版日期2024-11-01
卷号155页码:11
关键词3D reconstruction Multi-granularity Cycle loss High-fidelity
ISSN号0031-3203
DOI10.1016/j.patcog.2024.110647
英文摘要Monocular image -based 3D reconstruction is widely used in virtual reality, augmented reality, and autonomous driving, which benefits from the rapid development of deep learning approaches. Most of the available methods focused on reconstructing the overall shape of the object while ignoring some fine-grained details. Moreover, these methods make it hard to exactly reconstruct complex topological structures. In this paper, we propose a multi -granularity relationship reasoning network (MGRRNet), which aims to recover 3D shapes with high fidelity and rich details via the relationship reasoning between different granularity information. Specifically, our model captures the discriminative and detailed features at different granularities for extracting attentional regions. Then we perform the relationship reasoning between different granularities to reinforce the multi -granularity consistency and inter -granularity correlation. By doing this, our network is able to achieve robust feature representation and fine reconstruction. During the learning process, we jointly optimize procedures of different granularity feature representations via a sequence of inter -granularity cycle loss iterations. Extensive experimental results on two publicly available datasets justify that our approach achieves competitive performance compared to the state-of-the-art methods. Codes and all resources will be publicly available at https://github.com/Ray-tju/MGRRNet.
资助项目National Natural Science Foundation of China[62062056] ; Ningxia Graduate Education and Teaching Reform Research and Practice Project 2021
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001251884000001
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/39898]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wu, Suping
作者单位1.Ningxia Univ, Sch Informat Engn, Yinchuan 750021, Peoples R China
2.Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China
3.Ningxia Med Univ, Gen Hosp, Yinchuan 750003, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Li, Lei,Zhou, Zhiyuan,Wu, Suping,et al. Multi-granularity relationship reasoning network for high-fidelity 3D shape reconstruction[J]. PATTERN RECOGNITION,2024,155:11.
APA Li, Lei,Zhou, Zhiyuan,Wu, Suping,Li, Pan,&Zhang, Boyang.(2024).Multi-granularity relationship reasoning network for high-fidelity 3D shape reconstruction.PATTERN RECOGNITION,155,11.
MLA Li, Lei,et al."Multi-granularity relationship reasoning network for high-fidelity 3D shape reconstruction".PATTERN RECOGNITION 155(2024):11.

入库方式： OAI收割

来源：计算技术研究所

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