Capturing indoor scenes with smartphones

In this paper, we present a novel smartphone application designed to easily capture, visualize and reconstruct homes, offices and other indoor scenes. Our application leverages data from smartphone sensors such as the camera, accelerometer, gyroscope and magnetometer to help model the indoor scene. The output of the system is two-fold; first, an interactive visual tour of the scene is generated in real time that allows the user to explore each room and transition between connected rooms. Second, with some basic interactive photogrammetric modeling the system generates a 2D floor plan and accompanying 3D model of the scene, under a Manhattan-world assumption. The approach does not require any specialized equipment or training and is able to produce accurate floor plans.

[1]  Richard Szeliski,et al.  Reconstructing building interiors from images , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[2]  Tom Drummond,et al.  Going out: robust model-based tracking for outdoor augmented reality , 2006, 2006 IEEE/ACM International Symposium on Mixed and Augmented Reality.

[3]  Seth J. Teller,et al.  Extracting textured vertical facades from controlled close-range imagery , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  Richard Szeliski,et al.  Image-based interactive exploration of real-world environments , 2004, IEEE Computer Graphics and Applications.

[5]  Paul A. Zandbergen,et al.  Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning , 2009 .

[6]  Richard Szeliski,et al.  Creating full view panoramic image mosaics and environment maps , 1997, SIGGRAPH.

[7]  Andrew Lippman,et al.  Movie-maps: An application of the optical videodisc to computer graphics , 1980, SIGGRAPH '80.

[8]  Hojung Cha,et al.  Unsupervised Construction of an Indoor Floor Plan Using a Smartphone , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Alan L. Yuille,et al.  Manhattan World: compass direction from a single image by Bayesian inference , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  Yasushi Yagi,et al.  Real-time omnidirectional image sensor (COPIS) for vision-guided navigation , 1994, IEEE Trans. Robotics Autom..

[11]  Frederick P. Brooks,et al.  Walkthrough—a dynamic graphics system for simulating virtual buildings , 1987, I3D '86.

[12]  W. Buxton,et al.  Boom chameleon: simultaneous capture of 3D viewpoint, voice and gesture annotations on a spatially-aware display , 2002, UIST '02.

[13]  Christian Früh,et al.  Google Street View: Capturing the World at Street Level , 2010, Computer.

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

[15]  David Salesin,et al.  Schematic storyboarding for video visualization and editing , 2006, SIGGRAPH '06.

[16]  Young Min Kim,et al.  Interactive acquisition of residential floor plans , 2012, 2012 IEEE International Conference on Robotics and Automation.

[17]  Camillo J. Taylor,et al.  VideoPlus: A Method for Capturing the Structure and Appearance of Immersive Environments , 2000, IEEE Trans. Vis. Comput. Graph..

[18]  Hiroshi Ishiguro,et al.  Omnidirectional visual information for navigating a mobile robot , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[19]  Ian D. Reid,et al.  Manhattan scene understanding using monocular, stereo, and 3D features , 2011, 2011 International Conference on Computer Vision.

[20]  William Buxton,et al.  Boom chameleon: simultaneous capture of 3D viewpoint, voice and gesture annotations on a spatially-aware display , 2003, ACM Trans. Graph..

[21]  Marianne Bradnock Google art project , 2011 .