Mismatched: Evaluating the Limits of Image Matching Approaches and Benchmarks
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arXiv
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| Format: | Preprint |
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2024
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| author | Bonilla, Sierra Di Vece, Chiara Daher, Rema Ju, Xinwei Stoyanov, Danail Vasconcelos, Francisco Bano, Sophia |
| author_facet | Bonilla, Sierra Di Vece, Chiara Daher, Rema Ju, Xinwei Stoyanov, Danail Vasconcelos, Francisco Bano, Sophia |
| contents | Three-dimensional (3D) reconstruction from two-dimensional images is an active research field in computer vision, with applications ranging from navigation and object tracking to segmentation and three-dimensional modeling. Traditionally, parametric techniques have been employed for this task. However, recent advancements have seen a shift towards learning-based methods. Given the rapid pace of research and the frequent introduction of new image matching methods, it is essential to evaluate them. In this paper, we present a comprehensive evaluation of various image matching methods using a structure-from-motion pipeline. We assess the performance of these methods on both in-domain and out-of-domain datasets, identifying key limitations in both the methods and benchmarks. We also investigate the impact of edge detection as a pre-processing step. Our analysis reveals that image matching for 3D reconstruction remains an open challenge, necessitating careful selection and tuning of models for specific scenarios, while also highlighting mismatches in how metrics currently represent method performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16445 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mismatched: Evaluating the Limits of Image Matching Approaches and Benchmarks Bonilla, Sierra Di Vece, Chiara Daher, Rema Ju, Xinwei Stoyanov, Danail Vasconcelos, Francisco Bano, Sophia Computer Vision and Pattern Recognition Three-dimensional (3D) reconstruction from two-dimensional images is an active research field in computer vision, with applications ranging from navigation and object tracking to segmentation and three-dimensional modeling. Traditionally, parametric techniques have been employed for this task. However, recent advancements have seen a shift towards learning-based methods. Given the rapid pace of research and the frequent introduction of new image matching methods, it is essential to evaluate them. In this paper, we present a comprehensive evaluation of various image matching methods using a structure-from-motion pipeline. We assess the performance of these methods on both in-domain and out-of-domain datasets, identifying key limitations in both the methods and benchmarks. We also investigate the impact of edge detection as a pre-processing step. Our analysis reveals that image matching for 3D reconstruction remains an open challenge, necessitating careful selection and tuning of models for specific scenarios, while also highlighting mismatches in how metrics currently represent method performance. |
| title | Mismatched: Evaluating the Limits of Image Matching Approaches and Benchmarks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.16445 |