Acquisition, Representation, Processing and Display of Digital Heritage Sites

This chapter presents salient aspects of the research undertaken on the project Acquisition, Representation, Processing and Display of Digital Heritage Sites. The main objective of the project was to create algorithms and techniques to acquire a three-dimensional digital replica of complex structures spread over a large area. The techniques developed are applied to Hampi, a world heritage site. In addition to acquiring the geometry and surface properties, we also research efficient representation and visualisation of this data and provide tools and methods for users to experience the captured models, to virtual walk-through and explore the digital recreations. For the acquisition, we rely on multimodal input using technologies like laser scanners, colour cameras and depth sensors. We align and fuse geometric constructions from different modalities through a step of registration. We have extended structure from motion (SfM), a state-of-the-art approach for multi-view 3D reconstruction from images and developed techniques for large-scale (relatively) sparse geometric constructions and simultaneously dense reconstructions of smaller parts. We also provide ability to generate high-resolution point cloud from the point cloud obtained from depth camera Kinect by using additional high definition cameras. We also explore efficient visualisation of large models with augmented reality and user experience authoring. Hampi has been chosen as a test bench for developing our techniques. Within Hampi, we concentrate on Vittala Temple Complex, and demonstrate our techniques on it. The project has greatly benefitted from the collaboration from other partner institutes especially BVBCET, NID, NIAS, IIT Bombay, IIT Madras and IIIT Hyderabad and IISc Bangalore.

[1]  Nadia Magnenat-Thalmann,et al.  Real-time animation of ancient Roman sites , 2006, GRAPHITE '06.

[2]  Michal Havlena,et al.  Efficient Structure from Motion by Graph Optimization , 2010, ECCV.

[3]  Jan-Michael Frahm,et al.  Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs , 2008, International Journal of Computer Vision.

[4]  P. Kalra,et al.  Interactive Image Restoration Using Inpainting and Denoising , 2011, 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics.

[5]  George Papagiannakis,et al.  Mixing virtual and real scenes in the site of ancient Pompeii , 2005, Comput. Animat. Virtual Worlds.

[6]  Stephen J. Maybank,et al.  A Method for Interactive 3D Reconstruction of Piecewise Planar Objects from Single Images , 1999, BMVC.

[7]  Ian D. Reid,et al.  Single View Metrology , 2000, International Journal of Computer Vision.

[8]  Prem Kumar Kalra,et al.  Mixed reality based interaction system for digital heritage , 2016, VRCAI.

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

[10]  A. M. Day,et al.  Exploring cultural heritage sites through space and time , 2008, JOCCH.

[11]  Steven M. Seitz,et al.  Multicore bundle adjustment , 2011, CVPR 2011.

[12]  Subhajit Sanyal,et al.  Multilevel modelling and rendering of architectural scenes , 2003, Eurographics.

[13]  Changchang Wu,et al.  SiftGPU : A GPU Implementation of Scale Invariant Feature Transform (SIFT) , 2007 .

[14]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  Mark R. Stevens,et al.  Methods for Volumetric Reconstruction of Visual Scenes , 2004, International Journal of Computer Vision.

[16]  Ruigang Yang,et al.  Spatial-Depth Super Resolution for Range Images , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[18]  Kiriakos N. Kutulakos,et al.  A Theory of Shape by Space Carving , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[19]  Sebastian Thrun,et al.  An Application of Markov Random Fields to Range Sensing , 2005, NIPS.

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

[21]  Ken-ichi Anjyo,et al.  Tour into the picture: using a spidery mesh interface to make animation from a single image , 1997, SIGGRAPH.

[22]  Pascal Monasse,et al.  Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion , 2013, ICCV.

[23]  Richard Szeliski,et al.  Bundle Adjustment in the Large , 2010, ECCV.

[24]  Sebastian Thrun,et al.  LidarBoost: Depth superresolution for ToF 3D shape scanning , 2009, CVPR.

[25]  Luc Van Gool,et al.  Image-based procedural modeling of facades , 2007, SIGGRAPH 2007.

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

[27]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Changchang Wu,et al.  Towards Linear-Time Incremental Structure from Motion , 2013, 2013 International Conference on 3D Vision.

[29]  Manolis I. A. Lourakis,et al.  Enforcing Scene Constraints in Single View Reconstruction , 2007, Eurographics.

[30]  Sebastian Thrun,et al.  High-quality scanning using time-of-flight depth superresolution , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[31]  Tomás Pajdla,et al.  3D with Kinect , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[32]  Subhashis Banerjee,et al.  Divide and Conquer: Efficient Large-Scale Structure from Motion Using Graph Partitioning , 2014, ACCV.

[33]  Abhinav Shukla,et al.  A grammar-based GUI for single view reconstruction , 2012, ICVGIP '12.

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

[35]  Andrew Owens,et al.  Discrete-continuous optimization for large-scale structure from motion , 2011, CVPR 2011.

[36]  Andrew Owens,et al.  Discrete-continuous optimization for large-scale structure from motion , 2011, CVPR.

[37]  Subodh Kumar,et al.  User-guided modulation of rendering techniques for detail inspection , 2014, 2014 International Conference on Computer Graphics Theory and Applications (GRAPP).

[38]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Richard Szeliski,et al.  Skeletal graphs for efficient structure from motion , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Andrea Fusiello,et al.  Structure-and-motion pipeline on a hierarchical cluster tree , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[41]  Jan-Michael Frahm,et al.  Building Rome on a Cloudless Day , 2010, ECCV.

[42]  Martin White,et al.  Exploring and Interacting with Virtual Museums , 2005 .

[43]  Ping Tan,et al.  A Global Linear Method for Camera Pose Registration , 2013, 2013 IEEE International Conference on Computer Vision.

[44]  Alexei A. Efros,et al.  Automatic photo pop-up , 2005, SIGGRAPH 2005.