Inter-Photon-Limited Videography
Andrew Xie, Dongyu Du, Sotiris Nousias, David B. Lindell, Kiriakos N. Kutulakos
Abstract
We consider the problem of imaging a dynamic scene when scene appearance variations can outpace photon arrivals. Under such conditions, a pixel is effectively "blind" to changes in appearance that occur within the timespan separating the photons it detects, and so the inter-photon interval presents a significant speed barrier to video acquisition systems. To analyze and advance imaging capabilities at the inter-photon limit, we introduce a novel reparameterization of time-varying flux that reveals the intrinsic difficulty of signal reconstruction by relating the Fourier decomposition of a flux function to the number of photons arriving within each oscillation period. We find that inter-photon-limited videography of general scenes is underexplored and beyond the reach of existing reconstruction techniques. To this end, we introduce Neural Flux Fields---a technique that combines statistical modeling of photon arrival with intrinsic priors of a neural network to achieve robust videography at the inter-photon limit. Using this approach, we demonstrate never-before-seen capabilities in video reconstruction across a range of captured single-photon video datasets spanning the inter-photon-limited regime.
BibTeX
@inproceedings{cvpr2026_interphotonlimit,
title = {Inter-Photon-Limited Videography},
author = {Andrew Xie and Dongyu Du and Sotiris Nousias and David B. Lindell and Kiriakos N. Kutulakos},
booktitle = {CVPR 2026},
year = {2026}
}