Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
Three-dimensional reconstruction entails the development of mathematical models of three-dimensional objects that are suitable for computational representation and processing. This technique constructs realistic 3D models of images and has significant practical applications across various fields. Th...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/8/4088 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850183729784291328 |
|---|---|
| author | Lihong Yang Hang Ge Zhiqiang Yang Jia He Lei Gong Wanjun Wang Yao Li Liguo Wang Zhili Chen |
| author_facet | Lihong Yang Hang Ge Zhiqiang Yang Jia He Lei Gong Wanjun Wang Yao Li Liguo Wang Zhili Chen |
| author_sort | Lihong Yang |
| collection | DOAJ |
| description | Three-dimensional reconstruction entails the development of mathematical models of three-dimensional objects that are suitable for computational representation and processing. This technique constructs realistic 3D models of images and has significant practical applications across various fields. This study proposes a rapid and precise multi-view 3D reconstruction method to address the challenges of low reconstruction efficiency and inadequate, poor-quality point cloud generation in incremental structure-from-motion (SFM) algorithms in multi-view geometry. The methodology involves capturing a series of overlapping images of campus. We employed the Scale-invariant feature transform (SIFT) algorithm to extract feature points from each image, applied the KD-Tree algorithm for inter-image matching, and Enhanced autonomous threshold adjustment by utilizing the Random sample consensus (RANSAC) algorithm to eliminate mismatches, thereby enhancing feature matching accuracy and the number of matched point pairs. Additionally, we developed a feature-matching strategy based on similarity, which optimizes the pairwise matching process within the incremental structure from a motion algorithm. This approach decreased the number of matches and enhanced both algorithmic efficiency and model reconstruction accuracy. For dense reconstruction, we utilized the patch-based multi-view stereo (PMVS) algorithm, which is based on facets. The results indicate that our proposed method achieves a higher number of reconstructed feature points and significantly enhances algorithmic efficiency by approximately ten times compared to the original incremental reconstruction algorithm. Consequently, the generated point cloud data are more detailed, and the textures are clearer, demonstrating that our method is an effective solution for three-dimensional reconstruction. |
| format | Article |
| id | doaj-art-c3334c5a43144f9289ecee0079e29df0 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c3334c5a43144f9289ecee0079e29df02025-08-20T02:17:14ZengMDPI AGApplied Sciences2076-34172025-04-01158408810.3390/app15084088Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View ImagesLihong Yang0Hang Ge1Zhiqiang Yang2Jia He3Lei Gong4Wanjun Wang5Yao Li6Liguo Wang7Zhili Chen8College of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaCollege of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaCollege of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, China95841 Military Unit or Troop, Jiuquan 735000, ChinaCollege of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaCollege of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaCollege of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaCollege of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaCollege of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, ChinaThree-dimensional reconstruction entails the development of mathematical models of three-dimensional objects that are suitable for computational representation and processing. This technique constructs realistic 3D models of images and has significant practical applications across various fields. This study proposes a rapid and precise multi-view 3D reconstruction method to address the challenges of low reconstruction efficiency and inadequate, poor-quality point cloud generation in incremental structure-from-motion (SFM) algorithms in multi-view geometry. The methodology involves capturing a series of overlapping images of campus. We employed the Scale-invariant feature transform (SIFT) algorithm to extract feature points from each image, applied the KD-Tree algorithm for inter-image matching, and Enhanced autonomous threshold adjustment by utilizing the Random sample consensus (RANSAC) algorithm to eliminate mismatches, thereby enhancing feature matching accuracy and the number of matched point pairs. Additionally, we developed a feature-matching strategy based on similarity, which optimizes the pairwise matching process within the incremental structure from a motion algorithm. This approach decreased the number of matches and enhanced both algorithmic efficiency and model reconstruction accuracy. For dense reconstruction, we utilized the patch-based multi-view stereo (PMVS) algorithm, which is based on facets. The results indicate that our proposed method achieves a higher number of reconstructed feature points and significantly enhances algorithmic efficiency by approximately ten times compared to the original incremental reconstruction algorithm. Consequently, the generated point cloud data are more detailed, and the textures are clearer, demonstrating that our method is an effective solution for three-dimensional reconstruction.https://www.mdpi.com/2076-3417/15/8/4088feature extraction and matchingimage processingthree-dimensional reconstructionstructure from motion |
| spellingShingle | Lihong Yang Hang Ge Zhiqiang Yang Jia He Lei Gong Wanjun Wang Yao Li Liguo Wang Zhili Chen Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images Applied Sciences feature extraction and matching image processing three-dimensional reconstruction structure from motion |
| title | Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images |
| title_full | Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images |
| title_fullStr | Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images |
| title_full_unstemmed | Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images |
| title_short | Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images |
| title_sort | research on rapid and accurate 3d reconstruction algorithms based on multi view images |
| topic | feature extraction and matching image processing three-dimensional reconstruction structure from motion |
| url | https://www.mdpi.com/2076-3417/15/8/4088 |
| work_keys_str_mv | AT lihongyang researchonrapidandaccurate3dreconstructionalgorithmsbasedonmultiviewimages AT hangge researchonrapidandaccurate3dreconstructionalgorithmsbasedonmultiviewimages AT zhiqiangyang researchonrapidandaccurate3dreconstructionalgorithmsbasedonmultiviewimages AT jiahe researchonrapidandaccurate3dreconstructionalgorithmsbasedonmultiviewimages AT leigong researchonrapidandaccurate3dreconstructionalgorithmsbasedonmultiviewimages AT wanjunwang researchonrapidandaccurate3dreconstructionalgorithmsbasedonmultiviewimages AT yaoli researchonrapidandaccurate3dreconstructionalgorithmsbasedonmultiviewimages AT liguowang researchonrapidandaccurate3dreconstructionalgorithmsbasedonmultiviewimages AT zhilichen researchonrapidandaccurate3dreconstructionalgorithmsbasedonmultiviewimages |