Generic and Real Time Structure from Motion using Local Bundle Adjustment

This paper describes a method for estimating the motion of a calibrated camera and the three dimensional geometry of the filmed environment. The only data used is video input. Interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key frames are selected to enable 3D reconstruction of the features. We introduce a local bundle adjustment allowing 3D points and camera poses to be refined simultaneously through the sequence. This significantly reduces computational complexity when compared with global bundle adjustment. This method is applied initially to a perspective camera model, then extended to a generic camera model to describe most existing kinds of cameras. Experiments performed using real world data provide evaluations of the speed and robustness of the method. Results are compared to the ground truth measured with a differential GPS. The generalized method is also evaluated experimentally, using three types of calibrated cameras: stereo rig, perspective and catadioptric

Generic and Real-Time Structure from Motion

International audienceWe introduce a generic and incremental Structure from Motion method. By generic, we mean that the proposed method is independent of any specific camera model. During the incremental 3D reconstruction, parameters of 3D points and camera poses are refined simultaneously by a generic local bundle adjustment that minimizes an angular error between rays. This method has three main advantages: it is generic, fast and accurate. The proposed method is evaluated by experiments on real data with three kinds of calibrated cameras: stereo rig, perspective and catadioptric cameras

Bundle Adjustment in the Large

Abstract. We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjustment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are availabl