Extended monocular 3D imaging
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912228903485440 |
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| author | Shen, Zicheng Zhao, Feng Ni, Yibo Yang, Yuanmu |
| author_facet | Shen, Zicheng Zhao, Feng Ni, Yibo Yang, Yuanmu |
| contents | 3D vision is of paramount importance for numerous applications ranging from machine intelligence to precision metrology. Despite much recent progress, the majority of 3D imaging hardware remains bulky and complicated and provides much lower image resolution compared to their 2D counterparts. Moreover, there are many well-known scenarios that existing 3D imaging solutions frequently fail. Here, we introduce an extended monocular 3D imaging (EM3D) framework that fully exploits the vectorial wave nature of light. Via the multi-stage fusion of diffraction- and polarization-based depth cues, using a compact monocular camera equipped with a diffractive-refractive hybrid lens, we experimentally demonstrate the snapshot acquisition of a million-pixel and accurate 3D point cloud for extended scenes that are traditionally challenging, including those with low texture, being highly reflective, or nearly transparent, without a data prior. Furthermore, we discover that the combination of depth and polarization information can unlock unique new opportunities in material identification, which may further expand machine intelligence for applications like target recognition and face anti-spoofing. The straightforward yet powerful architecture thus opens up a new path for a higher-dimensional machine vision in a minimal form factor, facilitating the deployment of monocular cameras for applications in much more diverse scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extended monocular 3D imaging Shen, Zicheng Zhao, Feng Ni, Yibo Yang, Yuanmu Computer Vision and Pattern Recognition Optics 3D vision is of paramount importance for numerous applications ranging from machine intelligence to precision metrology. Despite much recent progress, the majority of 3D imaging hardware remains bulky and complicated and provides much lower image resolution compared to their 2D counterparts. Moreover, there are many well-known scenarios that existing 3D imaging solutions frequently fail. Here, we introduce an extended monocular 3D imaging (EM3D) framework that fully exploits the vectorial wave nature of light. Via the multi-stage fusion of diffraction- and polarization-based depth cues, using a compact monocular camera equipped with a diffractive-refractive hybrid lens, we experimentally demonstrate the snapshot acquisition of a million-pixel and accurate 3D point cloud for extended scenes that are traditionally challenging, including those with low texture, being highly reflective, or nearly transparent, without a data prior. Furthermore, we discover that the combination of depth and polarization information can unlock unique new opportunities in material identification, which may further expand machine intelligence for applications like target recognition and face anti-spoofing. The straightforward yet powerful architecture thus opens up a new path for a higher-dimensional machine vision in a minimal form factor, facilitating the deployment of monocular cameras for applications in much more diverse scenarios. |
| title | Extended monocular 3D imaging |
| topic | Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2502.07403 |