Dealing with Degenerate Input in 3D Modeling of Indoor Scenes using Handheld Cameras

3D models have many applications, but automatically building a 3D model from a video is a challenge in practice. Many methods exist for outdoor scenes, but indoor scenes are more difficult. Due to the limited movement, the input is very often close to degeneracy for which making a model is impossible without smart input processing. This paper presents our work toward a framework for modeling of indoor scenes. We first analyze the video to segment it into general and degenerate parts. From there specific auto-calibration methods can be applied to effectively solve the problem. Rather than ignoring degenerate segments, we develop a frame filtering method that preserves all the information of the input in order to achieve a more complete model. Results show that the remaining frames, significantly smaller in number, are nearly as informative as the original input and are suitable for the later steps of the modeling process.

[1]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[2]  Michael G. Strintzis,et al.  Efficient 3-D model search and retrieval using generalized 3-D radon transforms , 2006, IEEE Transactions on Multimedia.

[3]  Andrew W. Fitzgibbon,et al.  The Problem of Degeneracy in Structure and Motion Recovery from Uncalibrated Image Sequences , 1999, International Journal of Computer Vision.

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

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

[6]  Toby Howard,et al.  Interactive reconstruction of virtual environments from photographs, with application to scene-of-crime analysis , 2000, VRST '00.

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

[8]  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).

[9]  Paul Bao,et al.  A framework for remote rendering of 3-D scenes on limited mobile devices , 2006, IEEE Transactions on Multimedia.

[10]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[11]  Luc Van Gool,et al.  Surviving Dominant Planes in Uncalibrated Structure and Motion Recovery , 2002, ECCV.

[12]  Jean Ponce,et al.  On the absolute quadratic complex and its application to autocalibration , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  H. Opower Multiple view geometry in computer vision , 2002 .

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