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A Patch Prior for Dense 3D Reconstruction in Man-Made Environments

May 31, 2013 in ETHZ-CVG, Publications, year 2 by admin

Christian Haene, Christopher Zach, Bernhard Zeisl, and Marc Pollefeys

2012 2nd International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission

Dense 3D reconstruction in man-made environments has to contend with weak and ambiguous observations due to texture-less surfaces which are predominant in such environments. This challenging task calls for strong, domain-specific priors. These are usually modeled via regularization or smoothness assumptions. Generic smoothness priors, e.g. total variation are often not sufficient to produce convincing results. Consequently, we propose a more powerful prior directly modeling the expected local surface-structure, without the need to utilize expensive methods such as higher-order MRFs. Our approach is inspired by patch-based representations used in image processing. In contrast to the over-complete dictionaries used e.g. for sparse representations our patch dictionary is much smaller. The proposed energy can be optimized by utilizing an efficient first-order primal dual algorithm. Our formulation is in particular very natural to model priors on the 3D structure of man-made environments. We demonstrate the applicability of our prior on synthetic data and on real data, where we recover dense, piece-wise planar 3D models using stereo and fusion of multiple depth images.


@inproceedings{haene3DIMPVT13,
author = {Christian Haene and Christopher Zach and Bernhard Zeisl and Marc Pollefeys},
title = {A Patch Prior for Dense 3D Reconstruction in Man-Made Environments},
booktitle = {3DIMPVT},
year = {2013},
pages = {}
}

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Using Multi-Camera Systems in Robotics: Efficient Solutions to the NPnP Problem

March 15, 2013 in ETHZ-ASL, Publications, year 2 by admin

Laurent Kneip, Paul Timothy Furgale, Roland Siegwart

2013 IEEE International Conference on Robotics and Automation (ICRA)

This paper introduces two novel solutions to the generalized-camera exterior orientation problem, which has a vast number of potential applications in robotics: (i) a minimal solution requiring only three point correspondences, and (ii) gPnP, an efficient, non-iterative n-point solution with linear complexity in the number of points. Already existing minimal solutions require exhaustive algebraic derivations. In contrast, our novel minimal solution is solved in a straightforward manner using the Gro ̈bner basis method. Existing n-point solutions are mostly based on iterative optimization schemes. Our n-point solution is non-iterative and outperforms existing algorithms in terms of computational efficiency. Our results present an evaluation against state-of-the-art single-camera algorithms, and a comparison of different multi-camera setups. It demonstrates the superior noise resilience achieved when using multi-camera configurations, and the efficiency of our algorithms. As a further contribution, we illustrate a possible robotic use-case of our non-perspective orientation computation algorithms by presenting visual odometry results on real data with a non-overlapping multi-camera configuration, including a comparison to a loosely coupled alternative.


@inproceedings{kneip_icra13,
Address = {Karlsruhe, Germany},
Author = {Laurent Kneip and Paul Timothy Furgale and Roland Siegwart},
Booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), to appear.},
Month = {6-10 May},
Title = {Using Multi-Camera Systems in Robotics: Efficient Solutions to the NPnP Problem},
Year = {2013}
}

ICRA13_1065_FI.pdf

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