The paper presents a new analytical tool to solve the classical photogrammetric bundle block adjustment. The analytical model is based on the generalized extension of the anisotropic row-scaling Procrustes analysis, that has been recently proposed by the same authors to solve the image exterior orientation problem. The main advantage of the method is given by the fact that the problem solution ...

The task to compute 3D reconstructions from large amounts of data has become an active field of research within the last years. Based on an initial estimate provided by structure from motion, bundle adjustment seeks to find a solution that is optimal for all cameras and 3D points. The corresponding nonlinear optimization problem is usually solved by the Levenberg-Marquardt algorithm combined wi...

The traditional bundle adjustment algorithm for structure from motion problem has a computational complexity of O((m + n)) per iteration and memory requirement of O(mn(m+n)), where m is the number of cameras and n is the number of structure points. The sparse version of bundle adjustment has a computational complexity of O(m+mn) per iteration and memory requirement of O(mn). Here we propose an ...

Snakes are active contours in the 2d image plane. Starting from an initial estimate of a curve's shape and position they move to salient image features in an iterative process driven by minimization of an energy function. The twin snakes concept is an extension to traditional snakes. Twin snakes are designed for detecting two parallel contours simultaneously and are useful for line extraction i...

We propose an efficient parallel bundle adjustment (BA) algorithm to refine 3D reconstruction of the large-scale structure from motion (SfM) problem, which uses image collections from Internet. Different from the latest BA techniques that improve efficiency by optimizing the reprojection error function with Conjugate Gradient (CG) methods, we employ the parameter vector partition strategy. More...

Bundle Adjustment is a widely adopted self-calibration technique that allows to estimate scene structure and camera parameters at once. Typically this happens by iteratively minimizing the reprojection error between a set of 2D stereo correspondences and their predicted 3D positions. This optimization is almost invariantly carried out by means of the Levenberg-Marquardt algorithm, which is very...

Recent advances in 3D vision have demonstrated the strengths of photometric bundle adjustment. By directly minimizing reprojected pixel errors, instead of geometric reprojection errors, such methods can achieve sub-pixel alignment accuracy in both high and low textured regions. Typically, these problems are solved using a forwards compositional Lucas-Kanade [7] formulation parameterized by 6-Do...

This paper introduces a new bundle adjustment for multi-cameras, that simultaneously estimates not only the usual parameters (poses and points) but also the synchronization and the rolling shutter. We start from an initial calibration with a global shutter camera model (GS) and a frameaccurate synchronization provided by previous self-calibration methods [1]. Our BA provides subframe-accurate s...

Vision aiding of the navigation solution has become an integral component of low-cost IMU/GPS sub-systems providing direct georeferencing to remote sensing systems. The data workflow to recover the orientation parameters rigorously requires the simultaneous handling of large amount of imagery and navigation data. In some situations, with small unmanned aerial vehicles for example, a flight bloc...

Bundle adjustment is a key component of almost any feature based 3D reconstruction system, used to compute accurate estimates of calibration parameters and structure and motion configurations. These problems tend to be very large, often involving thousands of variables. Thus, efficient optimization methods are crucial. The traditional Levenberg Marquardt algorithm with a direct sparse solver ca...