TY - GEN
T1 - Focal Track
T2 - 16th IEEE International Conference on Computer Vision, ICCV 2017
AU - Guo, Qi
AU - Alexander, Emma
AU - Zickler, Todd
N1 - Funding Information:
This project was funded by US National Science Foundation awards IIS-1212928 and IIS-1718012, as well as NSF Graduate Research Fellowship No. DGE1144152 to E
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/22
Y1 - 2017/12/22
N2 - The focal track sensor is a monocular and computationally efficient depth sensor that is based on defocus controlled by a liquid membrane lens. It synchronizes small lens oscillations with a photosensor to produce real-time depth maps by means of differential defocus, and it couples these oscillations with bigger lens deformations that adapt the defocus working range to track objects over large axial distances. To create the focal track sensor, we derive a texture-invariant family of equations that relate image derivatives to scene depth when a lens changes its focal length differentially. Based on these equations, we design a feed-forward sequence of computations that: robustly incorporates image derivatives at multiple scales; produces confidence maps along with depth; and can be trained endto- end to mitigate against noise, aberrations, and other non-idealities. Our prototype with 1-inch optics produces depth and confidence maps at 100 frames per second over an axial range of more than 75cm.
AB - The focal track sensor is a monocular and computationally efficient depth sensor that is based on defocus controlled by a liquid membrane lens. It synchronizes small lens oscillations with a photosensor to produce real-time depth maps by means of differential defocus, and it couples these oscillations with bigger lens deformations that adapt the defocus working range to track objects over large axial distances. To create the focal track sensor, we derive a texture-invariant family of equations that relate image derivatives to scene depth when a lens changes its focal length differentially. Based on these equations, we design a feed-forward sequence of computations that: robustly incorporates image derivatives at multiple scales; produces confidence maps along with depth; and can be trained endto- end to mitigate against noise, aberrations, and other non-idealities. Our prototype with 1-inch optics produces depth and confidence maps at 100 frames per second over an axial range of more than 75cm.
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U2 - 10.1109/ICCV.2017.110
DO - 10.1109/ICCV.2017.110
M3 - Conference contribution
AN - SCOPUS:85041904611
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 966
EP - 974
BT - Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2017 through 29 October 2017
ER -