Mauricio Hess-Flores

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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(More)
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(More)
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(More)
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(More)
A comprehensive uncertainty, baseline, and noise analysis in computing 3D points using a recent L1-based triangulation algorithm is presented. This method is shown to be not only faster and more accurate than its main competitor, linear triangulation, but also more stable under noise and baseline changes. A Monte Carlo analysis of covariance and a(More)
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 error-based L<sub>1</sub> cost function which is more robust to outliers and less susceptible to local minima than cost functions such as(More)
This paper presents a novel, interactive visualization tool that allows for the analysis of scene structure uncertainty and its sensitivity to parameters in different multi-view scene reconstruction stages. Given a set of input cameras and feature tracks, the volume rendering-based approach first creates a scalar field from angular error measurements. The(More)
An algorithm that shows how ray divergence in multi-view stereo scene reconstruction can be used towards improving bundle adjustment weighting and conditioning is presented. Starting with a set of feature tracks, ray divergence when attempting to compute scene structure for each track is first obtained. Assuming accurate feature matching, ray divergence(More)
This paper presents a framework for GPU-accelerated N-view triangulation in multi-view reconstruction that improves processing time and final reprojection error with respect to methods in the literature. The framework uses an algorithm based on optimizing an angular error-based L<sub>1</sub> cost function and it is shown how adaptive gradient descent can be(More)
Shawn Recker 1,2, Mikhail M. Shashkov1, Mauricio Hess-Flores1, Christiaan Gribble2, Rob Baltrusch2 Mark A. Butkiewicz2, Kenneth I. Joy1 1Email: idav.ucdavis.edu/people Institute of Data Analysis and Visualization Department of Computer Science University of California Davis 1 Shields Ave, Davis, CA 95616 (530) 752-1077 2Email: {shawn.recker,(More)