Portable VXL system for computing structure from motion (original) (raw)

Survey of Structure from Motion

As a useful technology of 3D reconstruction based on binocular stereo vision, structure from motion is widely used in many fields and highly valuable in applications. However, few reviews have been focused on this technology. In this paper, the basic principles are overviewed. More specifically, the related works and main methods are discussed. Finally some future research directions are summarized.

A generic structure-from-motion framework

Computer Vision and Image Understanding, 2006

We introduce a generic structure-from-motion approach based on a previously introduced, highly general imaging model, where cameras are modeled as possibly unconstrained sets of projection rays. This allows to describe most existing camera types including pinhole cameras, sensors with radial or more general distortions, catadioptric cameras (central or non-central), etc. We introduce a structurefrom-motion approach for this general imaging model, that allows to reconstruct scenes from calibrated images, possibly taken by cameras of different types (cross-camera scenarios). Structure-from-motion is naturally handled via camera independent ray intersection problems, solved via linear or simple polynomial equations. We also propose two approaches for obtaining optimal solutions using bundle adjustment, where camera motion, calibration and 3D point coordinates are refined simultaneously. The proposed methods are evaluated via experiments on two cross-camera scenarios-a pinhole used together with an omni-directional camera and a stereo system used with an omni-directional camera.

Structure from motion with wide circular field of view cameras

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

This paper presents a method for fully automatic and robust estimation of two-view geometry, autocalibration, and 3D metric reconstruction from point correspondences in images taken by cameras with wide circular field of view. We focus on cameras which have more than 180 field of view and for which the standard perspective camera model is not sufficient, e.g., the cameras equipped with circular fish-eye lenses Nikon FC-E8 (183), Sigma 8mm-f4-EX (180), or with curved conical mirrors. We assume a circular field of view and axially symmetric image projection to autocalibrate the cameras. Many wide field of view cameras can still be modeled by the central projection followed by a nonlinear image mapping. Examples are the above-mentioned fish-eye lenses and properly assembled catadioptric cameras with conical mirrors. We show that epipolar geometry of these cameras can be estimated from a small number of correspondences by solving a polynomial eigenvalue problem. This allows the use of efficient RANSAC robust estimation to find the image projection model, the epipolar geometry, and the selection of true point correspondences from tentative correspondences contaminated by mismatches. Real catadioptric cameras are often slightly noncentral. We show that the proposed autocalibration with approximate central models is usually good enough to get correct point correspondences which can be used with accurate noncentral models in a bundle adjustment to obtain accurate 3D scene reconstruction. Noncentral camera models are dealt with and results are shown for catadioptric cameras with parabolic and spherical mirrors.

Robust recovery of the epipolar geometry for an uncalibrated stereo rig

Computer Vision—ECCV'94, 1994

This paper addresses the problem of accurately and automatically recovering the epipolar geometry from an uncalibrated stereo rig and its application to the image matching problem. A robust correlation based approach that eliminates outliers is developped to produce a reliable set of corresponding high curvature points. These points are used to estimate the so-called Fundamental Matrix which is closely related to the epipolar geometry of the uncalibrated stereo rig. We show that an accurate determination of this matrix is a central problem. Using a linear criterion in the estimation of this matrix is shown to yield erroneous results. Di erent parametrization and non-linear criteria are then developped to take into account the speci c constraints of the Fundamental Matrix providing more accurate results. Various experimental results on real images illustates the approach.

Improving Initial Estimations for Structure From Motion Methods

Proc. of the CESCG, 2009

In Computer Graphics as well as in Computer Vision and Autonomous Navigation, Structure from Motion is a com-mon method to register cameras. Usually several steps are involved with bundle-adjustment as the final one. A good initial estimation of camera positions is of ...

A parallel implementation of a structure-from-motion algorithm

1992

This paper describes the implementation of a 3D vision algorithm, Droid, on the Oxford parallel vision architecture, PARADOX, and the results of experiments to gauge the algorithm's effectiveness in providing navigation data for an autonomous guided vehicle. The algorithm reconstructs 3D structure by analysing image sequences obtained from a moving camera. In this application, the architecture delivers a performance of greater than 1 frame per second — 17 times the performance of a Sun-4 alone.

Structure from Motion with Known Camera Positions

2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)

The wide availability of GPS sensors is changing the landscape in the applications of structure from motion techniques for localization. In this paper, we study the problem of estimating camera orientations from multiple views, given the positions of the viewpoints in a world coordinate system and a set of point correspondences across the views. Given three or more views, the above problem has a finite number of solutions for three or more point correspondences. Given six or more views, the problem has a finite number of solutions for just two or more points. In the three-view case, we show the necessary and sufficient conditions for the three essential matrices to be consistent with a set of known baselines. We also introduce a method to recover the absolute orientations of three views in world coordinates from their essential matrices. To refine these estimates we perform a least-squares minimization on the group cross product SO(3) × SO(3) × SO(3). We report experiments on synthetic data and on data from the ICCV2005 Computer Vision Contest.

Camera models and fundamental concepts used in geometric computer vision

Foundations and Trends in Computer Graphics and Vision, 2010

This survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-developed without highlighting common underlying principles. One of the goals of this survey is to give an overview of image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, where we try to point out similarities between different models. Results on epipolar and multi-view geometry for different camera models are reviewed as well as various calibration and self-calibration approaches, with an emphasis on non-perspective cameras.We finally describe what we consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal here is to highlight the main principles of these, which are independent of specific camera models.

Self-Calibration of a Moving Camera from Point Correspondences and Fundamental Matrices

International Journal of Computer Vision, 1997

We address the problem of estimating three-dimensional motion, and structure from motion with an uncalibrated moving camera. We show that point correspondences between three images, and the fundamental matrices computed from these point correspondences, are sufficient to recover the internal orientation of the camera (its calibration), the motion parameters, and to compute coherent perspective projection matrices which enable us to reconstruct 3-D structure up to a similarity. In contrast with other methods, no calibration object with a known 3-D shape is needed, and no limitations are put upon the unknown motions to be performed or the parameters to be recovered, as long as they define a projective camera. The theory of the method, which is based on the constraint that the observed points are part of a static scene, thus allowing us to link the intrinsic parameters and the fundamental matrix via the absolute conic, is first detailed. Several algorithms are then presented, and their performances compared by means of extensive simulations and illustrated by several experiments with real images.

Match Selection and Refinement for Highly Accurate Two-View Structure from Motion

We present an approach to enhance the accuracy of structure from motion (SfM) in the two-view case. We first answer the question: "fewer data with higher accuracy, or more data with less accuracy?" For this, we establish a relation between SfM errors and a function of the number of matches and their epipolar errors. Using an accuracy estimator of individual matches, we then propose a method to select a subset of matches that has a good quality vs. quantity compromise. We also propose a variant of least squares matching to refine match locations based on a focused grid and a multi-scale exploration. Experiments show that both selection and refinement contribute independently to a better accuracy. Their combination reduces errors by a factor of 1.1 to 2.0 for rotation, and 1.6 to 3.8 for translation.