Sequential Reconstruction Segment-Wise Feature Track

1. Sequential Reconstruction Segment-Wise Feature Track and Structure Updating Based on Parallax Paths Mauricio Hess-Flores1, Mark A. Duchaineau2, Kenneth I. Joy3 1,3Institute for Data Analysis and Visualization, University of California, Davis, USA 2Lawrence Livermore National Laboratory, Livermore, CA, USA** 1mhessf@ucdavis.edu, 2duchaine@google.com, 3kenneth.i.joy@gmail.com **This author is now at Google, Inc. Abstract - 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. Algorithm (continued) Results Introduction I) Bundle adjustment convergence analysis. Total reprojection error ε in pixels, processing time t in seconds and iterations I of Levenberg-Marquardt, for bundle adjustment applied using the output of the proposed algorithm (PPBA) versus bundle adjustment applied using original feature tracks and structure (TBA), along with number of scene points NSP: Initial parallax path calculation (assuming known cameras) • Accurate 3D scene models obtained from aerial video can form a base for large-scale multi-sensor networks that support activities in detection, surveillance, tracking, registration, terrain modeling, and ultimately semantic scene analysis. • Due to varying lighting conditions, occlusions, repetitive patterns and other issues, feature tracks may not be perfect and this skews subsequent calibration and structure estimation. • 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, or a parallax path, when projected onto a parallel plane. This introduces two strong constraints, which differ from classical factorization and RANSAC, that can be used to detect and correct inaccurate feature tracks, while allowing for a very simple structure computation. • Computed per segment, relative to an anchor frame • Corrections are concatenated across consecutive segments Camera path Input images Position-invariant reference Structure • Each path on the reconstruction plane, computed for a given track, is placed in a position-invariant reference, where ideally each differs only by scale: II) Drift detection and track correction results (Dinosaur dataset): Inaccurate dense reconstruction Replica OcclusionsRepetitive patterns Parallax paths Top view of replicas Detected drift Original tracks Corrected tracks At the position-invariant reference, where paths only differ by scale s Original parallax paths III) Improvement in scene structure (Stockton aerial dataset): Inter and intra-camera constraints Algorithm • In this reference, inter-camera consensus path and intra-camera locus line constraints are defined, whose intersections (‘perfect grid’) predict how inaccurate tracks should be corrected: Algorithm flowchart Path differences from perfect grid Constrained paths Position-invariant reference Ray equation: Ray-plane intersection: Scaled paths Perfect grid Consensus path Locus line Structure computation after kth track correction Ct = (X0,Y0,Z0) = camera center at time t, Pt+ = projection matrix pseudo-inverse, xkt = pixel position for track k at time t, reconstruction plane = (A,B,C,D), Xkt = (Xd,Yd,Zd) = any 3D position along a ray Xk = computed 3D position, C1 = anchor camera center, sk = parallax scale, Tk,1 = corrected parallax path coordinates on the reconstruction plane for the anchor camera Original (left) versus corrected structure (right) This work was supported in part by the Department of Energy, National Nuclear Security Agency through Contract No. DE-GG52-09NA29355. This work was performed in part under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.