Abstract
While it is important to digitize heritage sites “as is”, building 3D models of damaged archaeological structures can be visually unpleasant due to the presence of large missing regions. In this chapter, we discuss geometric reconstruction of such large damaged regions (or holes) in 3D digital models. Without constraining the size or complexity of the damaged region, the missing 3D geometry inference problem is solved by making use of geometric prior from self-similar structures which provide a salient cue about the missing surface characteristics that may be unique to an object class. The underlying surface is then recovered by adaptively propagating 3D surface smoothness from local geometric information around the boundary of the hole and appropriately using the cue provided by the available self-similar examples. We have used two methodologies to effectively harness the geometric prior: (i) a non-iterative framework based on tensor voting when multiple self-similar examples are available and (ii) a dictionary learning-based method when only a single self-similar example is available. We showcase the relevance of our method in the archaeological domain which warrants “filling-in” missing information in damaged heritage sites. We show several examples from Hampi which is a UNESCO heritage site located in Northern Karnataka in India.
References
Aharon M, Elad M, Bruckstein A (2006) K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54:4311–4322
Andriy M, Xubo S (2010) Point set registration: Coherent point drift. IEEE Trans Pattern Anal Mach Intell 32:2262–2275. https://doi.org/10.1109/TPAMI.2010.46
Breckon TP, Fisher RB (2008) Three-dimensional surface relief completion via nonparametric techniques. IEEE Trans Pattern Anal Mach Intell 30:2249–2255
Callieri M, Cignoni P, Ganovelli F, Impoco G, Montani C, Pingi P, Ponchio F, Scopigno R (2004) Visualization and 3D data processing in the David restoration. IEEE Comput Gr Appl 24:16–21
Cignoni P, Corsini M, Ranzuglia G (2008) MeshLab: an Open-Source 3D Mesh Processing System. ERCIM News. 45–46
Davis J, Marschner S.R, Garr M, Levoy M (2002) Filling holes in complex surfaces using volumetric diffusion. 3D Data processing visualization and transmission. In: First International Symposium on. 428–438. https://doi.org/10.1109/TDPVT.2002.1024098
Geuzaine C, Remacle JF (2009) Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities. Int J Numer Method Eng 79:1309–1331
Gupta S, Castleman K.R, Markey M.K, Bovik A.C (2010) Texas 3D Face Recognition Database. In: Proc. 2010 SSIAI, TX2010, Austin, pp 97–100. https://doi.org/10.1109/SSIAI.2010.5483908
In: Google-Art-Project (2011) http://www.googleartproject.com/
In: INTACH (1984) http://www.intach.org/
In: Kinect (2010) http://www.xbox.com/en-IN/Kinect/
In: Photosynth (2010) http://photosynth.net
In: p3d (2013) http://p3d.in
Jia J, Tang CK (2004) Inference of segmented color and texture description by tensor voting. IEEE Trans Pattern Anal Mach Intell 26:771–786
Julien, M, Francis, B, Jean, P, Guillermo, S (2009) Online dictionary learning for sparse coding. ICML. pp 689–696
Kulkarni M, Rajagopalan AN, Rigoll G (2012) Depth Inpainting with Tensor Voting Using Local Geometry. In Proceedings, VISAPP
Kulkarni M, Rajagopalan AN (2013) Depth inpainting by tensor voting. J Opt Soc Am A opt Image Sci 30:1155–1165
Lai K, Bo L, Ren X, Fox D (2013) RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark. Consumer Depth Cameras for Computer Vision: Research Topics and Applications, pp 167–192
Levoy M, Pulli K, Curless B, Rusinkiewicz S, Koller D, Pereira L, Ginzton M, Anderson S, Davis J, Ginsberg J, Shade J, Fulk D (2000) The digital Michelangelo project: 3D scanning of large statues. ACM SIGGRAPH 2000:131–144
Liepa, P (2003) Filling holes in meshes. In: Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing. pp 200–205
Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60
Padalkar MG, Joshi MV (2015) Auto-inpainting heritage scenes: a complete framework for detecting and infilling cracks in images and videos with quantitative assessment. Mach Vis Appl 26:317–337
Patrick P, Michel G, Andrew B (2003) Poisson image editing. ACM Trans Graph 22:313–318
Pauly M, Mitra NJ, Giesen J, Gross M, Guibas LJ (2005) Example-based 3D scan completion. In: Proceedings of the third Eurographics symposium on Geometry processing
Philippos M, Grard M (2006) Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning. Synth Lect Image Video Multimed Process. 2:1–136
Scharstein D, Szeliski R (2002) A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. Int J Comput Vision 47:7–42
Shailaja, T (2013) Monuments gone missing, In: The Hindu. http://www.thehindu.com/features/metroplus/monuments-gone-missing
Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from RGBD images. ECCV. pp 746–760
Sturm, J, Engelhard, N, Endres, F, Burgard, W, Cremers, D (2012) A benchmark for the evaluation of RGB-D SLAM systems. In: Proceeding of the international conference on intelligent robot systems (IROS)
Szeliski R (2010) Computer Vision: Algorithms and Applications. Springer, New York
Tosic, I, Olshausen, B.A, Culpepper, B.J (2010) Learning sparse representations of depth. arXiv:1011.6656
UNESCO (2003) In: Draft charter on the preservation of the digital heritage. UNESCO General Conference 32nd session
Verdera, J, Caselles, V, Bertalmio, M, Sapiro, G (2003) Inpainting surface holes. In: Proceedings ICIP 2003, vol 2 II– pp 903–906
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Rajagopalan, A.N., Sahay, P., Vasu, S. (2017). A Methodology to Reconstruct Large Damaged Regions in Heritage Structures. In: Mallik, A., Chaudhury, S., Chandru, V., Srinivasan, S. (eds) Digital Hampi: Preserving Indian Cultural Heritage. Springer, Singapore. https://doi.org/10.1007/978-981-10-5738-0_10
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