The alignment of the spheres: Globally-optimal spherical mixture alignment for camera pose estimation

Dylan Campbell, Lars Petersson, Laurent Kneip, Hongdong Li, Stephen Gould

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    35 Citations (Scopus)

    Abstract

    Determining the position and orientation of a calibrated camera from a single image with respect to a 3D model is an essential task for many applications. When 2D-3D correspondences can be obtained reliably, perspective-n-point solvers can be used to recover the camera pose. However, without the pose it is non-trivial to find cross-modality correspondences between 2D images and 3D models, particularly when the latter only contains geometric information. Consequently, the problem becomes one of estimating pose and correspondences jointly. Since outliers and local optima are so prevalent, robust objective functions and global search strategies are desirable. Hence, we cast the problem as a 2D-3D mixture model alignment task and propose the first globally-optimal solution to this formulation under the robust L2 distance between mixture distributions. We derive novel bounds on this objective function and employ branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose estimate. To accelerate convergence, we integrate local optimization, implement GPU bound computations, and provide an intuitive way to incorporate side information such as semantic labels. The algorithm is evaluated on challenging synthetic and real datasets, outperforming existing approaches and reliably converging to the global optimum.

    Original languageEnglish
    Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
    PublisherIEEE Computer Society
    Pages11788-11798
    Number of pages11
    ISBN (Electronic)9781728132938
    DOIs
    Publication statusPublished - Jun 2019
    Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States
    Duration: 16 Jun 201920 Jun 2019

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Volume2019-June
    ISSN (Print)1063-6919

    Conference

    Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
    Country/TerritoryUnited States
    CityLong Beach
    Period16/06/1920/06/19

    Fingerprint

    Dive into the research topics of 'The alignment of the spheres: Globally-optimal spherical mixture alignment for camera pose estimation'. Together they form a unique fingerprint.

    Cite this