Abstract
We present a set of techniques to address the problem of scalable creation of visual overview representations of large 3D shape databases based on dimensionality reduction of feature vectors extracted from shape descriptions. We address the problem of feature extraction by exploring both combinations of hand-engineered geometric features and using the latent feature vectors generated by a deep learning classification method, and discuss the comparative advantages of both approaches. Separately, we address the problem of generating insightful 2D projections of these feature vectors that are able to separate well different groups of similar shapes by two approaches. First, we create quality projections by both automatic search in the space of feature combinations and, alternatively, by leveraging human insight to improve projections by iterative feature selection. Secondly, we use deep learning to automatically construct projections from the extracted features. We show that our three variations of deep learning, which jointly treat feature extraction, selection, and projection, allow efficient creation of high-quality visual overviews of large shape collections, require minimal user intervention, and are easy to implement. We demonstrate our approach on several real-world 3D shape databases.
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
Similar content being viewed by others
References
Aim@Shape: Aim@shape digital shape workbench 5.0 (2019). http://visionair.ge.imati.cnr.it
Belongie, S., Malik, J., Puzicha, J.: Shape context: a new descriptor for shape matching and object recognition. In: Proceedings of the NIPS, pp. 831–837 (2001)
Bustos, B., Keim, D., Saupe, D., Schreck, T., Vranic, D.: Feature-based similarity search in 3D object databases. ACM Comput. Surv. 37(4), 345–387 (2005)
Chen, X., Zeng, G., Kosinka, J., Telea, A.: Visual exploration of 3D shape databases via feature selection. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, pp. 42–53. INSTICC, SciTePress (2020). https://doi.org/10.5220/0008950700420053
Cyr, C.M., Kimia, B.B.: 3D object recognition using shape similiarity-based aspect graph. In: Proceedings of the IEEE ICCV, pp. 254–261 (2001)
Espadoto, M., Hirata, N., Telea, A.: Deep learning multidimensional projections. J. Inf. Vis. (2020). https://doi.org/10.1177/1473871620909485
Espadoto, M., Martins, R., Kerren, A., Hirata, N., Telea, A.: Towards a quantitative survey of dimension reduction techniques. IEEE TVCG (2019). https://doi.org/10.1109/TVCG.2019.2944182
Feng, C., Jalba, A.C., Telea, A.C.: Improved part-based segmentation of voxel shapes by skeleton cut spaces. Math. Morphol. - Theory Appl. 1(1) (2016)
ITI DB: The informatics & telematics institute database (2019). http://3d-search.iti.gr/3DSearch/index.html
Jalba, A., Kustra, J., Telea, A.: Computing surface and curve skeletons from large meshes on the GPU. IEEE TPAMI 35(6), 783–799 (2013)
Jalba, A., Kustra, J., Telea, A.: Surface and curve skeletonization of large 3D models on the GPU. IEEE TPAMI 35(6), 1495–1508 (2012)
Kalogerakis, E., Hertzmann, A., Singh, K.: Learning 3D mesh segmentation and labeling. ACM TOG 29(4) (2010)
van der Maaten, L., Hinton, G.: Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Martins, R., Coimbra, D., Minghim, R., Telea, A.: Visual analysis of dimensionality reduction quality for parameterized projections. Comput. Graph. 41, 26–42 (2014)
McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 (2018)
NASA: Nasa 3D resources (2019). https://nasa3d.arc.nasa.gov
Nonato, L., Aupetit, M.: Multidimensional projection for visual analytics: linking techniques with distortions, tasks, and layout enrichment. IEEE TVCG (2018). https://doi.org/10.1109/TVCG.2018.2846735
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., et al. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
Paulovich, F.V., Nonato, L.G., Minghim, R., Levkowitz, H.: Least square projection: a fast high-precision multidimensional projection technique and its application to document mapping. IEEE TVCG 14(3), 564–575 (2008)
Peyre, G., Cohen, L.: Geodesic computations for fast and accurate surface remeshing and parameterization. In: Bandle, C., et al. (eds.) Elliptic and Parabolic Problems. PNLDE, vol. 63, pp. 151–171. Springer, Heidelberg (2005). https://doi.org/10.1007/3-7643-7384-9_18
Pezzotti, N., Lelieveldt, B.P., van der Maaten, L., Höllt, T., Eisemann, E., Vilanova, A.: Approximated and user steerable t-SNE for progressive visual analytics. IEEE TVCG 23(7), 1739–1752 (2017)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Rauber, P.E., da Silva, R.R.O., Feringa, S., Celebi, M.E., Falcão, A.X., Telea, A.C.: Interactive image feature selection aided by dimensionality reduction. In: Proceedings of the EuroVA, pp. 19–23 (2015)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3212–3217 (2009)
Schmidt, W., Sotomayor, J., Telea, A., Silva, C., Comba, J.: A 3D shape descriptor based on depth complexity and thickness histograms. In: Proceedings of the SIBGRAPI (2015)
ShapeNet: ShapeNet online repository (2019). https://www.shapenet.org
Shapira, L., Shamir, A., Cohen-Or, D.: Consistent mesh partitioning and skeletonisation using the shape diameter function. Vis. Comput. 24(4), 249–262 (2008)
Shen, Y.T., Chen, D.Y., Tian, X.P., Ouhyoung, M.: 3D model search engine based on lightfield descriptors. In: Eurographics 2003 - Posters. Eurographics Association (2003). https://doi.org/10.2312/egp.20031031
Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton shape benchmark. In: Proceedings of the SMI, pp. 167–178 (2004). http://shape.cs.princeton.edu/benchmark
Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the IEEE Symposium on Visual Languages, pp. 336–343 (1996)
Shtrom, E., Leifman, G., Tal, A.: Saliency detection in large point sets. In: Proceedings of the IEEE ICCV, pp. 3591–3598 (2013)
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE ICCV, pp. 945–953 (2015)
Tangelder, J., Veltkamp, R.: A survey of content based 3D shape retrieval methods. Multimed. Tools Appl. 39(3), 441–471 (2008)
Tasse, F., Kosinka, J., Dodgson, N.: Cluster-based point set saliency. In: Proceedings of the IEEE ICCV, pp. 163–171 (2015)
Telea, A., Jalba, A.: Voxel-based assessment of printability of 3D shapes. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 393–404. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21569-8_34
Tukey, J., Tukey, P.: Computer graphics and exploratory data analysis: an introduction. In: The Collected Works of John W. Tukey: Graphics: 1965–1985 (1988)
TurboSquid Inc: Turbosquid shape repository (2019). https://www.turbosquid.com
Verma, V., Snoeyink, J.: Reducing the memory required to find a geodesic shortest path on a large mesh. In: Proceedings of the ACM GIS, pp. 227–235 (2009)
Wattenberg, M.: How to use t-SNE effectively (2016). https://distill.pub/2016/misread-tsne
Wilkinson, L., Anand, A., Grossman, R.: High-dimensional visual analytics: interactive exploration guided by pairwise views of point distributions. IEEE TVCG 12(6), 1363–1372 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, X., Zeng, G., Kosinka, J., Telea, A. (2022). Scalable Visual Exploration of 3D Shape Databases via Feature Synthesis and Selection. In: Bouatouch, K., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2020. Communications in Computer and Information Science, vol 1474. Springer, Cham. https://doi.org/10.1007/978-3-030-94893-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-94893-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94892-4
Online ISBN: 978-3-030-94893-1
eBook Packages: Computer ScienceComputer Science (R0)