This paper presents a framework for N -view triangulation of scene points, which improves processing time and final reprojection error with respect to standard methods, such as linear triangulation. The framework introduces an angular error-based cost function, which is robust to outliers and inexpensive to compute, and designed such that simple adaptive gradient descent can be applied for convergence. Our method also presents a statistical sampling component based on confidence levels, that reduces the number of rays to be used for triangulation of a given feature track. It is shown how the statistical component yields a meaningful yet much reduced set of representative rays for triangulation, and how the application of the cost function on the reduced sample can efficiently yield faster and more accurate solutions. Results are demonstrated on real and synthetic data, where it is proven to significantly increase the speed of triangulation and optimize reprojection error in most cases. This makes it especially attractive for efficient triangulation of large scenes given the speed and low memory requirements.

A correspondence and camera error analysis for dense correspondence applications such as structure from motion is introduced. This provides error introspection, opening up the possibility of adaptively and progressively applying more expensive correspondence and camera parameter estimation methods to reduce these errors. The presented algorithm evaluates the given correspondences and camera parameters based on an error generated through simple triangulation. This triangulation is based on the given dense, non-epipolar constraint, correspondences and estimated camera parameters. This provides an error map without requiring any information about the perfect solution or making assumptions about the scene. The resulting error is a combination of correspondence and camera parameter errors. An simple, fast low/high pass filter error factorization is introduced, allowing for the separation of correspondence error and camera error. Further analysis of the resulting error maps is applied to allow efficient iterative improvement of correspondences and cameras.

Abstract. This paper introduces a non-parametric sequential frame decimation algorithm for image sequences in low-memory streaming environments. Frame decimation reduces the number of input frames to increase pose and structure robustness in Structure and Motion (SaM) applications. The main contribution of this paper is the introduction of a sequential low-memory work-flow for frame decimation in embedded systems where memory and memory traffic come at a premium. This approach acts as an online preprocessing filter by removing frames that are ill-posed for reconstruction before streaming. The introduced sequential approach reduces the number of needed frames in memory to three in contrast to global frame decimation approaches that use at least ten frames in memory and is therefore suitable for low-memory streaming environments. This is moreover important in emerging systems with large format cameras which acquire data over several hours and therefore render global approaches impossible. In this paper a new decimation metric is designed which facilitates sequential keyframe extraction fit for reconstruction purposes, based on factors such as a correspondence-to-feature ratio and residual error relationships between epipolar geometry and homography estimation. The specific design of the error metric allows a local sequential decimation metric evaluation and can therefore be used on the fly. The approach has been tested with various types of input sequences and results in reliable low-memory frame decimation robust to different frame sampling frequencies and independent of any thresholds, scene assumptions or global frame analysis.

This paper presents a novel method for multi-view sequential scene reconstruction scenarios such as in aerial video, that exploits the constraints imposed by the path of a moving camera to allow for a new way of detecting and correcting inaccuracies in the feature tracking and structure computation processes. The main contribution of this paper is to show that for short, planar segments of a continuous camera trajectory, parallax movement corresponding to a viewed scene point should ideally form a scaled and translated version of this trajectory when projected onto a parallel plane. This creates two constraints, which differ from those of standard factorization, that allow for the detection and correction of inaccurate feature tracks and to improve scene structure. Results are shown for real and synthetic aerial video and turntable sequences, where the proposed method was shown to correct outlier tracks, detect and correct tracking drift, and allow for a novel improvement of scene structure, additionally resulting in an improved convergence for bundle adjustment optimization.

This paper presents a novel framework for practical and accurate N -view triangulation of scene points. The algorithm is based on applying swarm optimization inside a robustly-computed bounding box, using an angular errorbased L 1 cost function which is more robust to outliers and less susceptible to local minima than cost functions such as L 2 on reprojection error. Extensive testing on synthetic data with ground-truth has determined an accurate position over 99.9% of the time, on thousands of camera configurations with varying degrees of feature tracking errors. Opposed to existing polynomial methods developed for a small number of cameras, the proposed algorithm is at best linear in the number of cameras and does not suffer from inaccuracies inherent in solving high-order polynomials or Gröbner bases. In the specific case of three views, there is a two to three order of magnitude performance increase with respect to such methods. Results are provided to highlight performance for arbitrary camera configurations, numbers of cameras and under noise, which has not been previously achieved in the triangulation literature. Results on real data also prove that reprojection error is improved with respect to other methods.