Paper Info
Title
Photometric Bundle Adjustment for Dense Multi-view 3D Modeling
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 (ie depth map fusion, multi-view photometric stereo).
Year
DOI
Venue
2014
10.1109/CVPR.2014.193
CVPR
Keywords
DocType
ISSN
gradient methods,image fusion,image reconstruction,image representation,solid modelling,stereo image processing,3D multiview image reconstruction,3D reconstruction pipelines,3D reconstruction quality,Bayesian vision,camera parameters,dense multiview 3D modeling,depth map fusion,evolution equations,gradient descent scheme,multiview stereo,photometric bundle adjustment,photometric reprojection error,shape representation,triangular mesh,3D Modeling,Bundle Adjustment,Camera,Mesh,Multi-view Stereo,Surface Reconstruction
Conference
1063-6919
Citations 
PageRank 
References 
28
0.73
11
Authors
2
Name
Order
Citations
PageRank
Amaël Delaunoy11065.14
Marc Pollefeys27671475.90