Abstract
The rapid advance of the Internet provides available huge database of web images. In this paper, we introduce a novel approach for automatically computing the best views of 3D shapes based on their web images. Best view selection is generally an intuitive task of getting the most information of a 3D shape. The novelty of our approach is to directly explore human perception on observing 3D shapes from the relevant web images. Those images are captured from biased views of different people, thus sufficiently reflecting view choice when observing the 3D shapes. By collecting web images possibly captured from the similar views, the best view is selected as the one possessing the most web images. We experiment our method with the shapes in Princeton Shape Benchmark (PSB), as well make comparisons with traditional geometric descriptor based approaches. The results demonstrate that our method is not only robust but also able to produce more canonical views in accordance with human perception.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
McMillan, L., Bishop, G.: Plenoptic modeling: an image-based rendering system. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, pp. 39–46 (1995)
Denton, T., Demirci, M.F., Abrahamson, J., Shokoufandeh, A., Dickinson, S.: Selecting canonical views for view-based 3-d object recognition. In: International Conference on Pattern Recognition, vol. 2, pp. 273–276 (2004)
Mortara, M., Spagnuolo, M.: Semantics-driven best view of 3d shapes. Comput. Graph. 33(3), 280–290 (2009)
Blanz, V., Tarr, M.J., Bülthoff, H.H., Vetter, T.: What object attributes determine canonical views? Perception 28(5), 575–600 (1999)
Fu, H.B., Cohen-Or, D., Dror, G., Sheffer, A.: Upright orientation of man-made objects. ACM Trans. Graph. 27(3), 42–48 (2008)
Vázquez, P.P., Feixas, M., Sbert, M., Heidrich, W.: Viewpoint selection using viewpoint entropy. In: Proceedings of the Vision Modeling and Visualization Conference, pp. 273–280 (2001)
Vázquez, P.P., Feixas, M., Sbert, M., Llobet, A.: Viewpoint entropy: a new tool for obtaining good views of molecules. In: Proceedings of the Symposium on Data Visualisation, pp. 183–188 (2002)
Page, D.L., Koschan, A.F., Sukumar, S.R., Roui-Abidi, B., Abidi, M.A.: Shape analysis algorithm based on information theory. In: International Conference on Image Processing, vol. 1, pp. 229–232 (2003)
Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. ACM Trans. Graph. 24(3), 659–666 (2005)
Takahashi, S., Fujishiro, I., Takeshima, Y., Nishita, T.: A feature-driven approach to locating optimal viewpoints for volume visualization. In: IEEE Visualization, pp. 495–502 (2005)
Polonsky, O., Patané, G., Biasotti, S., Gotsman, C., Spagnuolo, M.: What’s in an image? Vis. Comput. 21(8), 840–847 (2005)
Laga, H.: Semantics-driven approach for automatic selection of best views of 3d shapes. In: Eurographics Workshop on 3D Object Retrieval (2010)
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Trans. Graph. 25(3), 835–846 (2006)
Hays, J., Efros, A.A.: Scene completion using millions of photographs. ACM Trans. Graph. 26(3) (2007)
Chen, T., Cheng, M.M., Tan, P., Shamir, A., Hu, S.M.: Sketch2photo: internet image montage. ACM Trans. Graph. 28(5), 124–133 (2009)
Raimondo, S., Gianluigi, C., Silvia, Z., Istituto, T., Infomatiche, M.: A survey of methods for colour image indexing and retrieval in image databases, pp. 183–211 (2001)
Schroff, F., Criminisi, A., Zisserman, A.: Harvesting image databases from the web. In: International Conference on Computer Vision, pp. 1–8 (2007)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)
Gooch, B., Hartner, M., Beddes, N.: Silhouette algorithm. In: ACM SIGGRAPH Course Notes (2003)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)
Connor, C., Egeth, H., Yantis, S.: Visual attention: bottom-up versus top-down. Curr. Biol. 14(19), 850–852 (2004)
Meyer, M., Desbrun, M., Schröder, P., Barr, A.H.: Discrete differential geometry operators for triangulated 2-manifolds. Math. Vis. 3(7), 1–26 (2002)
Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: ACM International Conference on Multimedia, pp. 815–824 (2006)
Cheng, M.M., Zhang, G.X., Mitra, N., Huang, X.L., Hu, S.M.: Global contrast based salient region detection. In: Computer Vision and Pattern Recognition (2011)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 603–619 (2002)
Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton shape benchmark. In: Proceedings of the Shape Modeling International, pp. 167–178 (2004)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, H., Zhang, L. & Huang, H. Web-image driven best views of 3D shapes. Vis Comput 28, 279–287 (2012). https://doi.org/10.1007/s00371-011-0638-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-011-0638-z