Automatic Camera Calibration and Scene Reconstruction with Scale-Invariant Features

The goal of our research is to robustly reconstruct general 3D scenes from 2D images, with application to automatic model generation in computer graphics and virtual reality. In this paper we aim at producing relatively dense and well-distributed 3D points which can subsequently be used to reconstruct the scene structure. We present novel camera calibration and scene reconstruction using scale-invariant feature points. A generic high-dimensional vector matching scheme is proposed to enhance the efficiency and reduce the computational cost while finding feature correspondences. A framework for structure and motion is also presented that better exploits the advantages of scale-invariant features. In this approach we solve the “phantom points” problem and this greatly reduces the possibility of error propagation. The whole process requires no information other than the input images. The results illustrate that our system is capable of producing accurate scene structure and realistic 3D models within a few minutes.

[1]  Marc Pollefeys,et al.  3D models from extended uncalibrated video sequences: addressing key-frame selection and projective drift , 2005, Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05).

[2]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Matthew A. Brown,et al.  Recognising panoramas , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Toby Howard,et al.  Interactive reconstruction of virtual environments from video sequences , 2003, Computers & graphics.

[6]  Reinhard Koch,et al.  3D Structure from Multiple Images of Large-Scale Environments , 1998, Lecture Notes in Computer Science.

[7]  Toby Howard,et al.  Accurate camera calibration for off-line, video-based augmented reality , 2002, Proceedings. International Symposium on Mixed and Augmented Reality.

[8]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  Maarten Vergauwen,et al.  Image-based 3D acquisition of archaeological heritage and applications , 2001, VAST '01.

[10]  David G. Lowe,et al.  Scene modelling, recognition and tracking with invariant image features , 2004, Third IEEE and ACM International Symposium on Mixed and Augmented Reality.

[11]  Andrew W. Fitzgibbon,et al.  Automatic 3D model acquisition and generation of new images from video sequences , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[12]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[13]  Andrew W. Fitzgibbon,et al.  Automatic 3D Model Construction for Turn-Table Sequences , 1998, SMILE.

[14]  Tamal K. Dey,et al.  Provable surface reconstruction from noisy samples , 2006, Comput. Geom..

[15]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[16]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[17]  Reinhard Koch,et al.  Visual Modeling with a Hand-Held Camera , 2004, International Journal of Computer Vision.

[18]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[19]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Rachid Deriche,et al.  A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry , 1995, Artif. Intell..