Towards 3D Vision with Low-Cost Single-Photon Cameras
Fangzhou Mu, Carter Sifferman, Sacha Jungerman, Yiquan Li, Mark Han, Michael Gleicher, Mohit Gupta, Yin Li
Abstract
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 offers a step towards 3D vision with single-photon cameras.
BibTeX
@inproceedings{cvpr2024_towards3dvisionw,
title = {Towards 3D Vision with Low-Cost Single-Photon Cameras},
author = {Fangzhou Mu and Carter Sifferman and Sacha Jungerman and Yiquan Li and Mark Han and Michael Gleicher and Mohit Gupta and Yin Li},
booktitle = {CVPR 2024},
year = {2024}
}