Due to technical advances, increasing amounts of 3D object become available in many important application domains like e.g., Computer Aided Design, Architecture and Cultural Heritage, Visualization, or Entertainment. To cope with large 3D data sets, one needs techniques to search for content based on appropriate similarity functions. In this course we study techniques for content-based searching in 3D object data. We will cover current methods for extraction of local and global 3D shape features, for coding of features and similarity computation, and for query processing including coverage of selected index structures. We will also cover approaches for visual search interfaces and for exploration of large 3D object repositories. The course also teaches how the search can be used in different application domains to support novel search and analysis tasks, including modeling by example or similarity-based object restoration. The students acquire knowledge of algorithms for content-based 3D similarity search, including feature extraction, query processing and user search. The students learn to implement basic 3D feature extraction methods. Over the course, students will learn how to build and evaluate a 3D retrieval system. The lecture comprises 1 hour exercises accompanying the lecture. In this exercise the students will implement, in teams of up to 2-3 students, a basic 3D search algorithm and apply it on provided test data. The exercise results will be graded based on an interactive presentation session. Depending on the number of participants, the lecture exam will be held as a written or oral exam at the end of the semester. The final grade for the course will be the average of the exercise and exam grade.
B. Bustos, D. Keim, D. Saupe, T. Schreck, and D. Vranic. Feature-based similarity search in 3D object databases. ACM Computing Surveys, 37(4):345–387, 2005.
J.L. Dugelay, A. Baskurt, M. Daoudi, and M. Daoudi. 3D Object Processing: Compression, Indexing and Watermarking. John Wiley & Sons, 2008.
T. Funkhouser, M. Kazhdan, P. Shilane, P. Min, W. Kiefer, a. Tal, S. Rusinkiewicz, and D. Dobkin. Modeling by example. ACM Transactions on Graphics (Proc. SIGGRAPH), August 2004.
R. Gregor, I. Sipiran, G. Papaioannou, T. Schreck, A. Andreadis, and P. Mavridis. Towards automated 3D reconstruction of defective cultural heritage objects. In Proc. EG Workshop on Graphics and Cultural Heritage, pages 135–144. Eurographics, 2014.
N. Iyer, S. Jayanti, K. Lou, Y. Kalyanaraman, and K. Ramani. Three-dimensional shape searching: state-of-the-art review and future trends. Comput. Aided Des., 37(5):509–530, April 2005.
B. Li, Y. Lu, A. Godil, T. Schreck, B. Bustos, A. Ferreira, T. Furuya, M. Fonseca, H. Johan, T. Matsuda, R. Ohbuchi, P. Pascoal, and J. Saavedra. A comparison of methods for sketch-based 3D shape retrieval. Elsevier Computer Vision and Image Understanding, 119:57–80, February 2014.
N. Pears, Y. Liu, and P. Bunting. 3D Imaging, Analysis and Applications. Springer London, 2012.
M. Savelonas, I. Pratikakis, and K. Sﬁkas. An overview of partial 3d object retrieval methodologies. Multimedia Tools and Applications, Springer, pages 1–26, 2014.
J. Tangelder and R. Veltkamp. A survey of content based 3d shape retrieval methods. Multimedia Tools Appl., 39(3):441–471, 2008.
Photo credits: 3D Object Retrieval. Dejan Vranic, Dissertation University of Leipzig, 2005. // B. Bustos, D. Keim, D. Saupe, T. Schreck, and D. Vranic. An experimental effectiveness comparison of methods for 3D similarity search. Springer International Journal on Digital Libraries, Special Issue on Multimedia Contents and Management, 6(1):39-54, 2006. // S.-M. Yoon, G.-J. Yoon, and T. Schreck. User-drawn sketch-based 3D object retrieval using sparse coding. Springer Multimedia Tools and Applications, 74(13):4707-4722, 2015.