Hassnae Remmach, Raja Mouachi, M. Sadgal, Aziz El Fazziki
{"title":"Reg-PointNet++: A CNN Network Based on PointNet++ Architecture for 3D Reconstruction of 3D Objects Modeled by Supershapes","authors":"Hassnae Remmach, Raja Mouachi, M. Sadgal, Aziz El Fazziki","doi":"10.18178/joig.11.4.405-413","DOIUrl":null,"url":null,"abstract":"The use of 3D reconstruction in computer vision applications has opened up new avenues for research and development. It has a significant impact on a range of industries, from healthcare to robotics, by improving the performance and abilities of computer vision systems. In this paper we aim to improve 3D reconstruction quality and accuracy. The objective is to develop a model that can learn to extract features, estimate a Supershape parameters and reconstruct 3D directly from input points cloud. In this regard, we present a continuity of our latest works, using a CNN-based Multi-Output and Multi-Task Regressor, for 3D reconstruction from 3D point cloud. We propose another new approach in order to refine our previous methodology and expand our findings. It is about “Reg-PointNet++”, which is mainly based on a PointNet++ architecture adapted for multi-task regression, with the goal of reconstructing a 3D object modeled by Supershapes from 3D point cloud. Given the difficulties encountered in applying convolution to point clouds, our approach is based on the PointNet ++ architecture. It is used to extract features from the 3D point cloud, which are then fed into a Multi-task Regressor for predicting the Supershape parameters needed to reconstruct the shape. The approach has shown promising results in reconstructing 3D objects modeled by Supershapes, demonstrating improved accuracy and robustness to noise and outperforming existing techniques. Visually, the predicted shapes have a high likelihood with the real shapes, as well as a high accuracy rate in a very reasonable number of iterations. Overall, the approach presented in the paper has the potential to significantly improve the accuracy and efficiency of 3D reconstruction, enabling its use in a wider range of applications.","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"60 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国图象图形学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.18178/joig.11.4.405-413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
The use of 3D reconstruction in computer vision applications has opened up new avenues for research and development. It has a significant impact on a range of industries, from healthcare to robotics, by improving the performance and abilities of computer vision systems. In this paper we aim to improve 3D reconstruction quality and accuracy. The objective is to develop a model that can learn to extract features, estimate a Supershape parameters and reconstruct 3D directly from input points cloud. In this regard, we present a continuity of our latest works, using a CNN-based Multi-Output and Multi-Task Regressor, for 3D reconstruction from 3D point cloud. We propose another new approach in order to refine our previous methodology and expand our findings. It is about “Reg-PointNet++”, which is mainly based on a PointNet++ architecture adapted for multi-task regression, with the goal of reconstructing a 3D object modeled by Supershapes from 3D point cloud. Given the difficulties encountered in applying convolution to point clouds, our approach is based on the PointNet ++ architecture. It is used to extract features from the 3D point cloud, which are then fed into a Multi-task Regressor for predicting the Supershape parameters needed to reconstruct the shape. The approach has shown promising results in reconstructing 3D objects modeled by Supershapes, demonstrating improved accuracy and robustness to noise and outperforming existing techniques. Visually, the predicted shapes have a high likelihood with the real shapes, as well as a high accuracy rate in a very reasonable number of iterations. Overall, the approach presented in the paper has the potential to significantly improve the accuracy and efficiency of 3D reconstruction, enabling its use in a wider range of applications.
中国图象图形学报Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.20
自引率
0.00%
发文量
6776
期刊介绍:
Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics.
Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art.
Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.