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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).
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Contributor : Amaël Delaunoy Connect in order to contact the contributor
Submitted on : Wednesday, April 30, 2014 - 2:39:17 PM
Last modification on : Thursday, May 27, 2021 - 1:54:05 PM
Long-term archiving on: : Wednesday, July 30, 2014 - 1:20:29 PM


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  • HAL Id : hal-00985811, version 1


Amaël Delaunoy, Marc Pollefeys. Photometric Bundle Adjustment for Dense Multi-View 3D Modeling. 2014. ⟨hal-00985811⟩



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