Hash Indexing-Based Image Matching for 3D Reconstruction
Image matching is a basic task in three-dimensional reconstruction, which, in recent years, has attracted extensive attention in academic and industrial circles. However, when dealing with large-scale image datasets, these methods have low accuracy and slow speeds. To improve the effectiveness of mo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/7/4518 |
| _version_ | 1797608362570940416 |
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| author | Mingwei Cao Haiyan Jiang Haifeng Zhao |
| author_facet | Mingwei Cao Haiyan Jiang Haifeng Zhao |
| author_sort | Mingwei Cao |
| collection | DOAJ |
| description | Image matching is a basic task in three-dimensional reconstruction, which, in recent years, has attracted extensive attention in academic and industrial circles. However, when dealing with large-scale image datasets, these methods have low accuracy and slow speeds. To improve the effectiveness of modern image matching methods, this paper proposes an image matching method for 3D reconstruction. The proposed method can obtain high matching accuracy through hash index in a very short amount of time. The core of hash matching includes two parts: creating the hash table and hash index. The former is used to encode local feature descriptors into hash codes, and the latter is used to search candidates for query feature points. In addition, the proposed method is extremely robust to image scaling and transformation by using various verifications. A comprehensive experiment was carried out using several challenging datasets to evaluate the performance of hash matching. Experimental results show that the HashMatch presents excellent results compared to the state-of-the-art methods in both computational efficiency and matching accuracy. |
| first_indexed | 2024-03-11T05:42:25Z |
| format | Article |
| id | doaj.art-f6ce3484ad8046ea8de2ee0416a07dee |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:42:25Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f6ce3484ad8046ea8de2ee0416a07dee2023-11-17T16:21:29ZengMDPI AGApplied Sciences2076-34172023-04-01137451810.3390/app13074518Hash Indexing-Based Image Matching for 3D ReconstructionMingwei Cao0Haiyan Jiang1Haifeng Zhao2Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, ChinaAnhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, ChinaAnhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, ChinaImage matching is a basic task in three-dimensional reconstruction, which, in recent years, has attracted extensive attention in academic and industrial circles. However, when dealing with large-scale image datasets, these methods have low accuracy and slow speeds. To improve the effectiveness of modern image matching methods, this paper proposes an image matching method for 3D reconstruction. The proposed method can obtain high matching accuracy through hash index in a very short amount of time. The core of hash matching includes two parts: creating the hash table and hash index. The former is used to encode local feature descriptors into hash codes, and the latter is used to search candidates for query feature points. In addition, the proposed method is extremely robust to image scaling and transformation by using various verifications. A comprehensive experiment was carried out using several challenging datasets to evaluate the performance of hash matching. Experimental results show that the HashMatch presents excellent results compared to the state-of-the-art methods in both computational efficiency and matching accuracy.https://www.mdpi.com/2076-3417/13/7/45183D reconstructionimage matchingstructure from motionlocal featurefeature matching |
| spellingShingle | Mingwei Cao Haiyan Jiang Haifeng Zhao Hash Indexing-Based Image Matching for 3D Reconstruction Applied Sciences 3D reconstruction image matching structure from motion local feature feature matching |
| title | Hash Indexing-Based Image Matching for 3D Reconstruction |
| title_full | Hash Indexing-Based Image Matching for 3D Reconstruction |
| title_fullStr | Hash Indexing-Based Image Matching for 3D Reconstruction |
| title_full_unstemmed | Hash Indexing-Based Image Matching for 3D Reconstruction |
| title_short | Hash Indexing-Based Image Matching for 3D Reconstruction |
| title_sort | hash indexing based image matching for 3d reconstruction |
| topic | 3D reconstruction image matching structure from motion local feature feature matching |
| url | https://www.mdpi.com/2076-3417/13/7/4518 |
| work_keys_str_mv | AT mingweicao hashindexingbasedimagematchingfor3dreconstruction AT haiyanjiang hashindexingbasedimagematchingfor3dreconstruction AT haifengzhao hashindexingbasedimagematchingfor3dreconstruction |