Photometric Bundle Adjustment for Dense Multi-View 3D Modeling

Amael Delaunoy, Marc Pollefeys; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1486-1493

Abstract


Motivated by a Bayesian vision of the 3D multi-view reconstruction from images problem, we propose a dense 3D reconstruction technique that jointly refines the shape and the camera parameters of a scene by minimizing the photometric reprojection error between a generated model and the observed images, hence considering all pixels in the original images. The minimization is performed using a gradient descent scheme coherent with the shape representation (here a triangular mesh), where we derive evolution equations in order to optimize both the shape and the camera parameters. This can be used at a last refinement step in 3D reconstruction pipelines and helps improving the 3D reconstruction's quality by estimating the 3D shape and camera calibration more accurately. Examples are shown for multi-view stereo where the texture is also jointly optimized and improved, but could be used for any generative approaches dealing with multi-view reconstruction settings (i.e. depth map fusion, multi-view photometric stereo).

Related Material


[pdf]
[bibtex]
@InProceedings{Delaunoy_2014_CVPR,
author = {Delaunoy, Amael and Pollefeys, Marc},
title = {Photometric Bundle Adjustment for Dense Multi-View 3D Modeling},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}