Evangelos Kalogerakis
Skip Abstract Section
Skip Supplemental Material SectionAbstract
We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis.
Supplemental Material
Available for Download
zip
a55-kalogerakis.zip (205.7 MB)
Supplemental material.
- Aliaga, D. G., Rosen, P. A., and Bekins, D. R. 2007. Style grammars for interactive visualization of architecture. IEEE Transactions on Visualization and Computer Graphics 13, 4. Google Scholar
Digital Library
- Allen, B., Curless, B., and Popović, Z. 2003. The space of human body shapes: reconstruction and parameterization from range scans. ACM Transactions on Graphics 22, 3. Google ScholarDigital Library
- Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., and Davis, J. 2005. SCAPE: shape completion and animation of people. ACM Transactions on Graphics 24, 3. Google ScholarDigital Library
- Blanz, V., and Vetter, T. 1999. A morphable model for the synthesis of 3D faces. In Proc. SIGGRAPH, ACM. Google ScholarDigital Library
- Bokeloh, M., Wand, M., and Seidel, H.-P. 2010. A connection between partial symmetry and inverse procedural modeling. ACM Transactions on Graphics 29, 4. Google ScholarDigital Library
- Bouchard, G., and Triggs, B. 2005. Hierarchical part-based visual object categorization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition. Google ScholarDigital Library
- Chaudhuri, S., and Koltun, V. 2010. Data-driven suggestions for creativity support in 3D modeling. ACM Transactions on Graphics 29, 6. Google ScholarDigital Library
- Chaudhuri, S., Kalogerakis, E., Guibas, L., and Koltun, V. 2011. Probabilistic reasoning for assembly-based 3D modeling. ACM Transactions on Graphics 30, 4. Google ScholarDigital Library
- Cheeseman, P., and Stutz, J. 1996. Bayesian classification (autoclass): Theory and results. Advances in Knowledge Discovery and Data Mining. Google ScholarDigital Library
- Chen, D.-Y., Tian, X.-P., Shen, Y.-T., and Ouhyoung, M. 2003. On visual similarity based 3D model retrieval. Computer Graphics Forum 22, 3.Google ScholarCross Ref
- Chennubhotla, C., and Jepson, A. 2001. S-PCA: Extracting multi-scale structure from data. In Proc. International Conference on Computer Vision.Google Scholar
- Fidler, S., and Leonardis, A. 2007. Towards scalable representations of object categories: Learning a hierarchy of parts. In Proc. IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
- Fu, H., Cohen-Or, D., Dror, G., and Sheffer, A. 2008. Upright orientation of man-made objects. ACM Transactions on Graphics 27, 3. Google ScholarDigital Library
- Funkhouser, T., Kazhdan, M., Shilane, P., Min, P., Kiefer, W., Tal, A., Rusinkiewicz, S., and Dobkin, D. 2004. Modeling by example. ACM Transactions on Graphics 23, 3. Google ScholarDigital Library
- Huang, Q., Koltun, V., and Guibas, L. 2011. Joint shape segmentation with linear programming. ACM Transactions on Graphics 30, 6. Google ScholarDigital Library
- Jain, A., Thormahlen, T., Ritschel, T., and Seidel, H.-P. 2012. Exploring shape variations by 3D-model decomposition and part-based recombination. Computer Graphics Forum 31, 2. Google ScholarDigital Library
- Jin, Y., and Geman, S. 2006. Context and hierarchy in a probabilistic image model. In Proc. IEEE Conference on Computer Vision and Pattern Recognition. Google ScholarDigital Library
- Kalogerakis, E., Hertzmann, A., and Singh, K. 2010. Learning 3D mesh segmentation and labeling. ACM Transactions on Graphics 29, 4. Google ScholarDigital Library
- Koller, D., and Friedman, N. 2009. Probabilistic Graphical Models: Principles and Techniques. The MIT Press. Google ScholarDigital Library
- Kraevoy, V., Julius, D., and Sheffer, A. 2007. Model composition from interchangeable components. In Proc. Pacific Graphics, IEEE Computer Society. Google ScholarDigital Library
- Lee, J., and Funkhouser, T. 2008. Sketch-based search and composition of 3D models. In Proc. Eurographics Workshop on Sketch-Based Interfaces and Modeling. Google ScholarDigital Library
- Merrell, P., and Manocha, D. 2011. Model synthesis: A general procedural modeling algorithm. IEEE Transactions on Visualization and Computer Graphics 17, 6. Google ScholarDigital Library
- Merrell, P. 2007. Example-based model synthesis. In Proc. Symposium on Interactive 3D Graphics, ACM. Google ScholarDigital Library
- Ommer, B., and Buhmann, J. 2010. Learning the compositional nature of visual object categories for recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 3. Google ScholarDigital Library
- Ovsjanikov, M., Li, W., Guibas, L., and Mitra, N. J. 2011. Exploration of continuous variability in collections of 3D shapes. ACM Transactions on Graphics 30, 4. Google ScholarDigital Library
- Ranzato, M. A., Susskind, J., Mnih, V., and Hinton, G. 2011. On deep generative models with applications to recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition. Google ScholarDigital Library
- Roux, N. L., Heess, N., Shotton, J., and Winn, J. 2011. Learning a generative model of images by factoring appearance and shape. Neural Computation, 23. Google ScholarDigital Library
- Sharf, A., Blumenkrants, M., Shamir, a., and Cohen-Or, D. 2006. SnapPaste: an interactive technique for easy mesh composition. Visual Computer 22, 9. Google ScholarDigital Library
- Sidi, O., van Kaick, O., Kleiman, Y., Zhang, H., and Cohen-Or, D. 2011. Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering. ACM Transactions on Graphics 30, 6. Google ScholarDigital Library
- Stava, O., Beneš, B., Měch, R., Aliaga, D., and Kristof, P. 2010. Inverse procedural modeling by automatic generation of L-systems. Computer Graphics Forum 29, 2.Google Scholar
- Todorovic, S., and Ahuja, N. 2008. Unsupervised category modeling, recognition, and segmentation in images. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 12. Google ScholarDigital Library
- Tu, Z., Chen, X., Yuille, A. L., and Zhu, S.-C. 2005. Image parsing: Unifying segmentation, detection, and recognition. International Journal of Computer Vision 63, 2. Google ScholarDigital Library
- Wessel, R., Blümel, I., and Klein, R. 2009. A 3d shape benchmark for retrieval and automatic classification of architectural data. In Eurographics 2009 Workshop on 3D Object Retrieval. Google ScholarDigital Library
- Xu, K., Zheng, H., Zhang, H., Cohen-Or, D., Liu, L., and Xiong, Y. 2011. Photo-inspired model-driven 3D object modeling. ACM Transactions on Graphics 30, 4. Google ScholarDigital Library
- Zhu, L. L., Lin, C., Huang, H., Chen, Y., and Yuille, A. L. 2008. unsupervised structure learning: Hierarchical recursive composition, suspicious coincidence and competitive exclusion. In Proc. European Conference on Computer Vision. Google ScholarDigital Library
Index Terms
- A probabilistic model for component-based shape synthesis
Recommendations
Discrete curve model for non-elastic shape analysis on shape manifold
Highlights- We construct a shape manifold and its tangent space for the discrete curve model.
AbstractIn this paper, we construct a novel finite dimensional shape manifold for shape analyses. Elements of the shape manifold are a set of discrete, planar, and closed curves, which stand for object boundaries and are represented by ...
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking
WWW '16: Proceedings of the 25th International Conference on World Wide WebMany fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic ...
Structured probabilistic inference
Probabilistic inference is among the main topics with reasoning in uncertainty in AI. For this purpose, Bayesian Networks (BNs) is one of the most successful and efficient Probabilistic Graphical Model (PGM) so far. Since the mid-90s, a growing number ...
Comments