Towards 3D Vision with Low-Cost Single-Photon Cameras
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909155419226112 |
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| author | Mu, Fangzhou Sifferman, Carter Jungerman, Sacha Li, Yiquan Han, Mark Gleicher, Michael Gupta, Mohit Li, Yin |
| author_facet | Mu, Fangzhou Sifferman, Carter Jungerman, Sacha Li, Yiquan Han, Mark Gleicher, Michael Gupta, Mohit Li, Yin |
| contents | We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras, operating as time resolved image sensors, illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process, account for its non-idealities, and adapt neural rendering to reconstruct 3D geometry from a set of spatially distributed sensors with known poses. We show that our approach can successfully recover complex 3D shapes from simulated data. We further demonstrate 3D object reconstruction from real-world captures, utilizing measurements from a commodity proximity sensor. Our work draws a connection between image-based modeling and active range scanning and is a step towards 3D vision with single-photon cameras. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17801 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards 3D Vision with Low-Cost Single-Photon Cameras Mu, Fangzhou Sifferman, Carter Jungerman, Sacha Li, Yiquan Han, Mark Gleicher, Michael Gupta, Mohit Li, Yin Computer Vision and Pattern Recognition Image and Video Processing We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras, operating as time resolved image sensors, illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process, account for its non-idealities, and adapt neural rendering to reconstruct 3D geometry from a set of spatially distributed sensors with known poses. We show that our approach can successfully recover complex 3D shapes from simulated data. We further demonstrate 3D object reconstruction from real-world captures, utilizing measurements from a commodity proximity sensor. Our work draws a connection between image-based modeling and active range scanning and is a step towards 3D vision with single-photon cameras. |
| title | Towards 3D Vision with Low-Cost Single-Photon Cameras |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2403.17801 |