Title :
Photometric Bundle Adjustment for Dense Multi-view 3D Modeling
Author :
Delaunoy, Amael ; Pollefeys, Marc
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).
Keywords :
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; Calibration; Cameras; Equations; Image reconstruction; Mathematical model; Shape; Three-dimensional displays; 3D Modeling; Bundle Adjustment; Camera; Mesh; Multi-view Stereo; Surface Reconstruction;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
Conference_Location :
Columbus, OH
DOI :
10.1109/CVPR.2014.193