Non-Learning Low-Light Stereo Vision
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866918532596367360 |
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| author | Wang, Jason Nguyen, Lucas Eom, Hyunseung Xu, Wei Guo, Qi |
| author_facet | Wang, Jason Nguyen, Lucas Eom, Hyunseung Xu, Wei Guo, Qi |
| contents | We present a non-learning stereo framework for disparity estimation from severely noisy images. Using the Field of Junctions (FoJ), it retains coarse visual features stable under severe noise for cost volume construction while discarding fine textures inseparable from photon noise. The resulting structural information guides boundary-aware Semi-Global Matching (SGM) that dynamically adapts smoothness penalties to preserve true disparity discontinuities. The output is a sparse disparity map more accurate than those of recent stereo algorithms over unmasked pixels on widely-used benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00379 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Non-Learning Low-Light Stereo Vision Wang, Jason Nguyen, Lucas Eom, Hyunseung Xu, Wei Guo, Qi Computer Vision and Pattern Recognition We present a non-learning stereo framework for disparity estimation from severely noisy images. Using the Field of Junctions (FoJ), it retains coarse visual features stable under severe noise for cost volume construction while discarding fine textures inseparable from photon noise. The resulting structural information guides boundary-aware Semi-Global Matching (SGM) that dynamically adapts smoothness penalties to preserve true disparity discontinuities. The output is a sparse disparity map more accurate than those of recent stereo algorithms over unmasked pixels on widely-used benchmark datasets. |
| title | Non-Learning Low-Light Stereo Vision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2606.00379 |