Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment

被引:252
作者
Furukawa, Yasutaka [1 ]
Ponce, Jean [2 ]
机构
[1] Univ Washington, Seattle, WA 98195 USA
[2] ENS INRIA CNRS, Willow Project Team, Lab Informat, UMR 8548, F-75230 Paris 05, France
基金
美国国家科学基金会;
关键词
Bundle adjustment; Structure from motion; Multi-view stereo; Image-based modeling; Camera calibration;
D O I
10.1007/s11263-009-0232-2
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
The advent of high-resolution digital cameras and sophisticated multi-view stereo algorithms offers the promise of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on camera calibration to fulfill these promises. This paper presents a novel approach to camera calibration where top-down information from rough camera parameter estimates and the output of a multi-view-stereo system on scaled-down input images is used to effectively guide the search for additional image correspondences and significantly improve camera calibration parameters using a standard bundle adjustment algorithm (Lourakis and Argyros 2008). The proposed method has been tested on six real datasets including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and objects with high curvature and thin structures that are lost in visual hull construction even with small errors in camera parameters. Three different methods have been used to qualitatively assess the improvements of the camera parameters. The implementation of the proposed algorithm is publicly available at Furukawa and Ponce (2008b).
引用
收藏
页码:257 / 268
页数:12
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