@inproceedings{ab0d618ea2954c37a6e58da1b1188e94,
title = "Focal flow: Measuring distance and velocity with defocus and differential motion",
abstract = "We present the focal flow sensor. It is an unactuated, monocular camera that simultaneously exploits defocus and differential motion to measure a depth map and a 3D scene velocity field. It does so using an optical-flow-like, per-pixel linear constraint that relates image derivatives to depth and velocity. We derive this constraint, prove its invariance to scene texture, and prove that it is exactly satisfied only when the sensor{\textquoteright}s blur kernels are Gaussian. We analyze the inherent sensitivity of the ideal focal flow sensor, and we build and test a prototype. Experiments produce useful depth and velocity information for a broader set of aperture configurations, including a simple lens with a pillbox aperture.",
author = "Emma Alexander and Qi Guo and Sanjeev Koppal and Steven Gortler and Todd Zickler",
note = "Funding Information: We would like to thank J Zachary Gaslowitz and Ioannis Gkioulekas for helpful discussion. This work was supported by a gift from Texas Instruments Inc. and by the National Science Foundation under awards No. IIS-1212928 and 1514154 and Graduate Research Fellowship No. DGE1144152 to E.A. Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 14th European Conference on Computer Vision, ECCV 2016 ; Conference date: 08-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46487-9_41",
language = "English (US)",
isbn = "9783319464862",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "667--682",
editor = "Bastian Leibe and Jiri Matas and Nicu Sebe and Max Welling",
booktitle = "Computer Vision - 14th European Conference, ECCV 2016, Proceedings",
}