LMI-based 2D-3D Registration: from Uncalibrated Images to Euclidean Scene - Archive ouverte HAL Access content directly
Conference Papers Year :

LMI-based 2D-3D Registration: from Uncalibrated Images to Euclidean Scene


This paper investigates the problem of registering a scanned scene, represented by 3D Euclidean point coordinates , and two or more uncalibrated cameras. An unknown subset of the scanned points have their image projections detected and matched across images. The proposed approach assumes the cameras only known in some arbitrary projective frame and no calibration or autocalibration is required. The devised solution is based on a Linear Matrix Inequality (LMI) framework that allows simultaneously estimating the projective transformation relating the cameras to the scene and establishing 2D-3D correspondences without triangulating image points. The proposed LMI framework allows both deriving triangulation-free LMI cheirality conditions and establishing putative correspondences between 3D volumes (boxes) and 2D pixel coordinates. Two registration algorithms, one exploiting the scene's structure and the other concerned with robustness, are presented. Both algorithms employ the Branch-and-Prune paradigm and guarantee convergence to a global solution under mild initial bound conditions. The results of our experiments are presented and compared against other approaches.
Fichier principal
Vignette du fichier
2145.pdf (804.21 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01149529 , version 1 (07-05-2015)
hal-01149529 , version 2 (17-05-2015)
hal-01149529 , version 3 (08-06-2015)


  • HAL Id : hal-01149529 , version 3


Danda Pani Paudel, Adlane Habed, Cedric Demonceaux, Pascal Vasseur. LMI-based 2D-3D Registration: from Uncalibrated Images to Euclidean Scene. CVPR 2015 - 28th IEEE Conference on Computer Vision and Pattern Recognition, Jun 2015, Boston, United States, Jun 2015, Boston, United States. ⟨hal-01149529v3⟩
698 View
385 Download


Gmail Facebook Twitter LinkedIn More