Abstract
Virtual reconstruction of heritage is one of the most interesting and innovative tool for preservation and keeping of historical, architectural and artistic memory of many sites that are in danger of disappearing. Find the best way to present an object in virtual reality is necessary for reasons linked to technology itself. In particular, the rendering of heavy object, in terms of details and meshes, influences the presentation of the whole virtual scene. Different researches have shown the onset of problems such as sickness due to an incorrect construction and representation of virtual scenes. In this paper we propose a 3D points cloud reconstruction method based on an optimization procedure by using genetic algorithm to improve the mesh obtained by low cost acquisition devices. The improved photogrammetric technique could be used to build virtual scenario by inexpensive devices (i.e. smartphone), without the cost and computational complexity of expensive devices.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guttentag, D.A.: Virtual reality: applications and implications for tourism. Tour. Manag. 31(5), 637–651 (2010)
Fritz, F., Susperregui, A., Linaza, M.T.: Enhancing cultural tourism experiences with augmented reality technologies. In: 6th International Symposium on Virtual Reality, Archaeology and Cultural Heritage (VAST) (2005)
Suveg, I., Vosselman, G.: 3D reconstruction of building models. Int. Arch. Photogrammetry Remote Sens. 33(B2; PART 2), 538–545 (2000)
Tangelder, J.W.H., Veltkamp, R.C.: A survey of content based 3D shape retrieval methods. Multimedia Tools Appl. 39(3), 441–471 (2008)
Remondino, F., El-Hakim, S.: Image-based 3D modelling: a review. Photogram. Rec. 21(115), 269–291 (2006)
Bevilacqua, V., Ivona, F., Cafarchia, D., Marino, F.: An evolutionary optimization method for parameter search in 3D points cloud reconstruction. In: Huang, D.-S., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds.) ICIC 2013. LNCS, vol. 7995, pp. 601–611. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39479-9_70
Guidi, G., Russo, M., Beraldin, J.A.: Acquisizione 3D e modellazione poligonale. McGraw-Hill, New York (2009)
Mikhail, E.M., Bethel, J.S., McGlone, J.C.: Introduction to modern photogrammetry, New York (2001)
Luhmann, T., Robson, S., Kyle, S., Harley, I.: Close range photogrammetry: principles, methods and applications. Whittles (2006)
Russo, M., Remondino, F.: Laser scanning e fotogrammetria: strumenti e metodi di rilievo tridimensionale per larcheologia. APSAT 1, 133–164 (2012)
Hagebeuker, B.: A 3D time of flight camera for object detection (2007)
Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J., Reynolds, J.M.: structure-from-motion photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179, 300–314 (2012)
Lowe, D.G.: Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on Computer vision, vol. 2, pp. 1150–1157. IEEE (1999)
Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment — a modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) IWVA 1999. LNCS, vol. 1883, pp. 298–372. Springer, Heidelberg (2000). doi:10.1007/3-540-44480-7_21
Chaikin, G.M.: An algorithm for high-speed curve generation. Comput. Graph. Image Process. 3(4), 346–349 (1974)
Kobbelt, L.: 3-subdivision. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 103–112. ACM Press/Addison-Wesley Publishing Co. (2000)
Micchelli, C.A., Prautzsch, H.: Uniform refinement of curves. Linear Algebra Appl. 114, 841–870 (1989)
Dyn, N., Levine, D., Gregory, J.A.: A butterfly subdivision scheme for surface interpolation with tension control. ACM Trans. Graph. 9(2), 160–169 (1990)
Catmull, E., Clark, J.: Recursively generated b-spline surfaces on arbitrary topological meshes. Comput. Aided Des. 10(6), 350–355 (1978)
Guennebaud, S.B., Schlick, C.: Least squares subdivision surfaces. Comput. Graph. Forum 29(7), 2021–2028 (2010)
Loop, C.: Smooth subdivision surfaces based on triangles (1987)
Habib, A., Warren, J.D.: Edge and vertex insertion for a class of C1 subdivision surfaces. Comput. Aided Geom. Design 16(4), 223–247 (1999)
Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer Science & Business Media, Heidelberg (2008)
Friedberg, R.M.: A learning machine: Part I. IBM J. Res. Dev. 2(1), 2–13 (1958)
Friedberg, R.M., Dunham, B., North, J.H.: A learning machine: Part II. IBM J. Res. Dev. 3(3), 282–287 (1959)
Bevilacqua, V., Brunetti, A., Triggiani, M., Magaletti, D., Telegrafo, M., Moschetta, M.: An optimized feed-forward artificial neural network topology to support radiologists in breast lesions classification. In: Genetic and Evolutionary Computation Conference, GECCO 2016, pp. 1385–1392. ACM (2016)
Bevilacqua, V., Mastronardi, G., Menolascina, F., Pannarale, P., Pedone, A.: A novel multi-objective genetic algorithm approach to artificial neural network topology optimisation: the breast cancer classification problem. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, pp. 1958–1965. IEEE (2006)
Aspert, N., Santa-Cruz, D., Ebrahimi, T.: MESH: measuring errors between surfaces using the hausdorff distance. In: Proceedings of the 2002 IEEE International Conference on Multimedia and Expo, vol. I, pp. 705–708. IEEE Computer Society (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bevilacqua, V. et al. (2017). Photogrammetric Meshes and 3D Points Cloud Reconstruction: A Genetic Algorithm Optimization Procedure. In: Rossi, F., Piotto, S., Concilio, S. (eds) Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry. WIVACE 2016. Communications in Computer and Information Science, vol 708. Springer, Cham. https://doi.org/10.1007/978-3-319-57711-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-57711-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57710-4
Online ISBN: 978-3-319-57711-1
eBook Packages: Computer ScienceComputer Science (R0)