Focal flow: Measuring distance and velocity with defocus and differential motion

Emma Alexander; Qi Guo; Sanjeev Koppal; Steven Gortler; Todd Zickler

doi:10.1007/978-3-319-46487-9_41

Focal flow: Measuring distance and velocity with defocus and differential motion

Emma Alexander^*, Qi Guo, Sanjeev Koppal, Steven Gortler, Todd Zickler

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

18 Scopus citations

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’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.

Original language	English (US)
Title of host publication	Computer Vision - 14th European Conference, ECCV 2016, Proceedings
Editors	Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
Publisher	Springer Verlag
Pages	667-682
Number of pages	16
ISBN (Print)	9783319464862
DOIs	https://doi.org/10.1007/978-3-319-46487-9_41
State	Published - 2016
Externally published	Yes
Event	14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands Duration: Oct 8 2016 → Oct 16 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9907 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	14th European Conference on Computer Vision, ECCV 2016
Country/Territory	Netherlands
City	Amsterdam
Period	10/8/16 → 10/16/16

Funding

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.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46487-9_41

Cite this

Alexander, E., Guo, Q., Koppal, S., Gortler, S., & Zickler, T. (2016). Focal flow: Measuring distance and velocity with defocus and differential motion. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision - 14th European Conference, ECCV 2016, Proceedings (pp. 667-682). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9907 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46487-9_41

Alexander, Emma ; Guo, Qi ; Koppal, Sanjeev et al. / Focal flow : Measuring distance and velocity with defocus and differential motion. Computer Vision - 14th European Conference, ECCV 2016, Proceedings. editor / Bastian Leibe ; Jiri Matas ; Nicu Sebe ; Max Welling. Springer Verlag, 2016. pp. 667-682 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

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Alexander, E, Guo, Q, Koppal, S, Gortler, S & Zickler, T 2016, Focal flow: Measuring distance and velocity with defocus and differential motion. in B Leibe, J Matas, N Sebe & M Welling (eds), Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9907 LNCS, Springer Verlag, pp. 667-682, 14th European Conference on Computer Vision, ECCV 2016, Amsterdam, Netherlands, 10/8/16. https://doi.org/10.1007/978-3-319-46487-9_41

Focal flow: Measuring distance and velocity with defocus and differential motion. / Alexander, Emma; Guo, Qi; Koppal, Sanjeev et al.
Computer Vision - 14th European Conference, ECCV 2016, Proceedings. ed. / Bastian Leibe; Jiri Matas; Nicu Sebe; Max Welling. Springer Verlag, 2016. p. 667-682 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9907 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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’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.

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Alexander E, Guo Q, Koppal S, Gortler S, Zickler T. Focal flow: Measuring distance and velocity with defocus and differential motion. In Leibe B, Matas J, Sebe N, Welling M, editors, Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Springer Verlag. 2016. p. 667-682. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46487-9_41

Focal flow: Measuring distance and velocity with defocus and differential motion

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