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Towards 3D Vision with Low-Cost Single-Photon Cameras

Fangzhou Mu · Carter Sifferman · Sacha Jungerman · Yiquan Li · Zhiyue Han · Michael Gleicher · Mohit Gupta · Yin Li

Arch 4A-E Poster #45
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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 an interesting connection between image-based modeling and active range scanning and is a step towards 3D vision with single-photon cameras.

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