by admin

Relative Pose Estimation for a Multi-Camera System with Known Vertical Direction

June 12, 2014 in ETHZ-CVG, Publications, year 3 by admin

Gim Hee Lee, Marc Pollefeys, and Friedrich Fraundorfer

2014 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)

In this paper, we present our minimal 4-point and linear 8-point algorithms to estimate the relative pose of a multi-camera system with known vertical directions, i.e. known absolute roll and pitch angles. We solve the minimal 4-point algorithm with the hidden variable resultant method and show that it leads to an 8-degree univariate polynomial that gives up to 8 real solutions. We identify a degenerated case from the linear 8-point algorithm when it is solved with the standard Singular Value Decomposition (SVD) method and adopt a simple alternative solution which is easy to implement. We show that our proposed algorithms can be efficiently used within RANSAC for robust estimation. We evaluate the accuracy of our proposed algorithms by comparisons with various existing algorithms for the multi-camera system on simulations and show the feasibility of our proposed algorithms with results from multiple real-world datasets.


@inproceedings{leeCVPR14,
author = {Gim Hee Lee and
Marc Pollefeys and
Friedrich Fraundorfer},
title = {Relative Pose Estimation for a Multi-Camera System with Known Vertical Direction},
booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2014},
pages = {}
}

by admin

Class Specific 3D Object Shape Priors Using Surface Normals

June 12, 2014 in ETHZ-CVG, Publications, year 3 by admin

Christian Haene, Nikolay Savinov, and Marc Pollefeys

2014 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)

Dense 3D reconstruction of real world objects containing textureless, reflective and specular parts is a challenging task. Using general smoothness priors such as surface area regularization can lead to defects in the form of disconnected parts or unwanted indentations. We argue that this problem can be solved by exploiting the object class specific local surface orientations, e.g. a car is always close to horizontal in the roof area. Therefore, we formulate an object class specific shape prior in the form of spatially varying anisotropic smoothness terms. The parameters of the shape prior are extracted from training data. We detail how our shape prior formulation directly fits into recently proposed volumetric multi-label reconstruction approaches. This allows a segmentation between the object and its supporting ground. In our experimental evaluation we show reconstructions using our trained shape prior on several challenging datasets.


@inproceedings{haeneCVPR14,
author = {Christian Haene and
Nikolay Savinov and
Marc Pollefeys},
title = {Class Specific 3D Object Shape Priors Using Surface Normals},
booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2014},
pages = {}
}

by admin

Unsupervised Learning of Threshold for Geometric Verification in Visual-Based Loop-Closure

June 12, 2014 in ETHZ-CVG, Publications, year 3 by admin

Gim Hee Lee, and Marc Pollefeys

2014 IEEE International Conference on Robotics and Automation (ICRA)

A potential loop-closure image pair passes the geometric verification test if the number of inliers from the computation of the geometric constraint with RANSAC exceed a pre-defined threshold. The choice of the threshold is critical to the success of identifying the correct loop-closure image pairs. However, the value for this threshold often varies for different datasets and is chosen empirically. In this paper, we propose an unsupervised method that learns the threshold for geometric verification directly from the observed inlier counts of all the potential loop-closure image pairs. We model the distributions of the inlier counts from all the potential loop-closure image pairs with a two components Log-Normal mixture model – one component represents the state of non loop-closure and the other represents the state of loop-closure, and learn the parameters with the Expectation-Maximization algorithm. The intersection of the Log-Normal mixture distributions is the optimal threshold for geometric verification, i.e. the threshold that gives the minimum false positive and negative loop-closures. Our algorithm degenerates when there are too few or no loop-closures and we propose the ^_chi-squared test to detect this degeneracy. We verify our proposed method with several large-scale datasets collected from both the multi-camera setup and stereo camera.


@inproceedings{leeICRA14,
author = {Gim Hee Lee and
Marc Pollefeys},
title = {Unsupervised Learning of Threshold for Geometric Verification in Visual-Based Loop-Closure},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {2014},
pages = {}
}

by admin

Infrastructure-Based Calibration of a Multi-Camera Rig

June 12, 2014 in ETHZ-ASL, ETHZ-CVG, Publications, year 3 by admin

Lionel Heng, Mathias Buerki, Gim Hee Lee, Paul Furgale, Roland Siegwart, and Marc Pollefeys

2014 IEEE International Conference on Robotics and Automation (ICRA)

The online recalibration of multi-sensor systems is a fundamental problem that must be solved before complex automated systems are deployed in situations such as automated driving. In such situations, accurate knowledge of calibration parameters is critical for the safe operation of automated systems. However, most existing calibration methods for multi-sensor systems are computationally expensive, use installations of known fiducial patterns, and require expert supervision. We propose an alternative approach called infrastructure-based calibration that is efficient, requires no modification of the infrastructure, and is completely unsupervised. In a survey phase, a computationally expensive simultaneous localization and mapping (SLAM) method is used to build a highly accurate map of a calibration area. Once the map is built, many other vehicles are able to use it for calibration as if it were a known fiducial pattern.

We demonstrate the effectiveness of this method to calibrate the extrinsic parameters of a multi-camera system. The method does not assume that the cameras have an overlapping field of view and it does not require an initial guess. As the camera rig moves through the previously mapped area, we match features between each set of synchronized camera images and the map. Subsequently, we find the camera poses and inlier 2D-3D correspondences. From the camera poses, we obtain an initial estimate of the camera extrinsics and rig poses, and optimize these extrinsics and rig poses via non-linear refinement. The calibration code is publicly available as a standalone C++ package.


@inproceedings{hengICRA14,
author = {Lionel Heng and
Mathias Buerki and
Gim Hee Lee and
Paul Furgale and
Roland Siegwart and
Marc Pollefeys},
title = {Infrastructure-Based Calibration of a Multi-Camera Rig},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {2014},
pages = {}
}