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We introduce a method for creating very dense reconstructions of datasets, particularly turn-table varieties. The method takes in initial reconstructions (of any origin) and makes them denser by interpolating depth values in two-dimensional image space within a superpixel region and then optimizing the interpolated value via image consistency analysis across neighboring images in the dataset. One...
Structure-from-Motion (SfM) applications attempt to reconstruct the three-dimensional (3D) geometry of an underlying scene from a collection of images, taken from various camera viewpoints. Traditional optimization techniques in SfM, which compute and refine camera poses and 3D structure, rely only on feature tracks, or sets of corresponding pixels, generated from color (RGB) images. With the abundance...
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 confidence ellipsoid analysis were performed over...
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 L1 cost function and it is shown how adaptive gradient descent can be applied for convergence. The triangulation algorithm...
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 L1 cost function which is more robust to outliers and less susceptible to local minima than cost functions such as L2 on reprojection error. Extensive testing on synthetic data...
Analyzing sources and causes of error in multi-view scene reconstruction is difficult. In the absence of any ground-truth information, reprojection error is the only valid metric to assess error. Unfortunately, inspecting reprojection error values does not allow computer vision researchers to attribute a cause to the error. A visualization technique to analyze errors in sequential multi-view reconstruction...
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...
This paper presents an interactive visualization system, based upon previous work, 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 creates a scalar field from reprojection error measurements. The obtained statistical,...
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