Towards 3D Vision with Low-Cost Single-Photon Cameras
<|reference_start|>Towards 3D Vision with Low-Cost Single-Photon Cameras: 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.<|reference_end|>
@article{mu2024towards,
    title={Towards 3D Vision with Low-Cost Single-Photon Cameras},
    author={Fangzhou Mu, Carter Sifferman, Sacha Jungerman, Yiquan Li, Mark Han,
  Michael Gleicher, Mohit Gupta, Yin Li},
    journal={arXiv preprint arXiv:2403.17801},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17801},
    primaryClass={cs.CV eess.IV}
}