This presentation reviews the following paper: Crivellaro, Alberto, and Vincent Lepetit. "Robust 3d tracking with descriptor fields." Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014

1. Sharif University of Technology
18th April Multi Camera Vision
Robust 3D Tracking with
Descriptor Fields
Presented By: Hossein Babashah
Multi Camera Vision Course
Professor: Dr.H.Aghajan
April 18th, 2015

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18th April Multi Camera Vision
Problem Definition
 Goal:3D Tracking register images from specular and poorly textured.
 Input:
◦ reference images
◦ partial 3D model

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Sharif University of Technology
18th April Multi Camera Vision
Introduction:
 3D tracking
◦ Applications:
 Robotics
 Augmented Reality

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Sharif University of Technology
18th April Multi Camera Vision
Introduction:
 3D tracking  poorly textured, specular(non
Lambertian)challenging

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18th April Multi Camera Vision
Related Works
 image contours based
 fragile in practice.
◦ perturbed by their reflections
 feature point-based
◦ robustbut only for textured & Lambertian env.
◦ e.g: PTAM

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18th April Multi Camera Vision
Related Works
 Dense Image Alignment
◦ growing computational power
◦ not limited to edge or keypoint features
◦ most image information. every pixel method.
◦ optimization algorithms
 Inverse Compositional Algorithm (ICA)
 Efficient Second- order Method (ESM)
◦ not robust to specularities
 to split the tracked surfacerobustness how to split an arbitrary surface? especially in
3D.
 exploit specularities on controlled environments.

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18th April Multi Camera Vision
The Proposed Article
Dense Image Alignment
Novel
Local
Descriptor
Non-Lambertian or
poor textured
YES
Intensity
Non-Lambertian
or poor textured
NO Multi Scale Optimization
Deteriorate Information

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18th April Multi Camera Vision
Dense Alignment for Camera Tracking
 Goal: estimate parameters p of a warp mapping a reference image T
into a warped one I
T
I
I(W(.,p))
T(.) - I(W(.,p))
Video from Alberto Crivellaro

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18th April Multi Camera Vision
Dense Alignment for Camera Tracking
 a multi-scale approach is used to optimize p
 use “local jets” for the d function (computed by convolving an image
with a series of filters)
 fi filters are typically Gaussian derivatives kernels

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18th April Multi Camera Vision
Dense Alignment for Camera Tracking

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Sharif University of Technology
18th April Multi Camera Vision
Experimental Results

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Sharif University of Technology
18th April Multi Camera Vision
Experimental Results
 Who is the winner?
◦ Rotation Error Translation Error

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18th April Multi Camera Vision
Conclusion
 Dense approaches with no linear dependency on image are better
methods than every other method mentioned
 In real life, one should be careful with the choice of the algorithm
and d function.

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18th April Multi Camera Vision
Refrences
 Crivellaro, Alberto, and Vincent Lepetit. "Robust 3d tracking with
descriptor fields." Computer Vision and Pattern Recognition (CVPR),
2014 IEEE Conference on. IEEE, 2014.
 G. Klein and D. Murray. Parallel Tracking and Mapping for Small AR
Workspaces. In ISMAR, November 2007.
 G. Scandaroli, M. Meilland, and R. Richa. Improving NCC- Based
Direct Visual Tracking. In ECCV, 2012.
 M. Nguyen and F. D. la Torre. Metric Learning for Image Alignment.
IJCV, 88(1), 2010.

15. Thank You!
Sharif University of Technology
18th April Multi Camera Vision