3D Reconstruction Using Multiple View Stereo and a Brief Introduction to Kinect

This chapter deals with the methodology of 3D reconstruction, both sparse and dense. The basic properties of the projective geometry and the camera models are introduced to understand the preliminaries about the subject. A more detail can be found in the book (Hartley & Zisserman, 2000). The sparse reconstruction deals with reconstructing 3D points for few image points. There are gaps in the reconstructed 3D points. Dense reconstruction tries to fill up gaps and make the density of the reconstruction higher. Estimation of correspondences is an integral part of multiview reconstruction and the author will discuss the point correspondences among images here. Finally the author will introduce the Microsoft Kinect, a divice which directly capture 3D information in realtime, and will show how to enhance the Kinect point cloud using vision framework.

[1]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[2]  Shafraz Subdurally,et al.  Counting People Using Blobs and Contours , 2013, Int. J. Comput. Vis. Image Process..

[3]  Manolis I. A. Lourakis,et al.  SBA: A software package for generic sparse bundle adjustment , 2009, TOMS.

[4]  Tae-Sun Choi,et al.  Depth Map and 3D Imaging Applications: Algorithms and Technologies , 2011 .

[5]  Gheorghita Ghinea,et al.  User Centered Design for Medical Visualization , 2008 .

[6]  Hui Zeng,et al.  Fast Rotation-Invariant DAISY Descriptor for Image Keypoint Matching , 2010, 2010 IEEE International Symposium on Multimedia.

[7]  Roberto Cipolla,et al.  Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[9]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[10]  Jean Ponce,et al.  Accurate, Dense, and Robust Multiview Stereopsis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Subhashis Banerjee,et al.  High Resolution Point Cloud Generation from Kinect and HD Cameras using Graph Cut , 2012, VISAPP.

[12]  Aly A. Farag,et al.  Assessment of Kidney Function Using Dynamic Contrast Enhanced MRI Techniques , 2010 .

[13]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[14]  Raja Guedouar,et al.  Forward Projection for Use with Iterative Reconstruction , 2012 .

[15]  Kiriakos N. Kutulakos,et al.  A Theory of Shape by Space Carving , 2000, International Journal of Computer Vision.

[16]  Eduardo Romero,et al.  Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques , 2009 .

[17]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Richard Szeliski,et al.  Building Rome in a day , 2009, ICCV.