research-article

Probabilistic reasoning for assembly-based 3D modeling

Authors:

Siddhartha Chaudhuri,

Evangelos Kalogerakis,

Leonidas Guibas,

Vladlen KoltunAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 30, Issue 4

Article No.: 35, Pages 1 - 10

https://doi.org/10.1145/2010324.1964930

Published: 25 July 2011 Publication History

Abstract

Assembly-based modeling is a promising approach to broadening the accessibility of 3D modeling. In assembly-based modeling, new models are assembled from shape components extracted from a database. A key challenge in assembly-based modeling is the identification of relevant components to be presented to the user. In this paper, we introduce a probabilistic reasoning approach to this problem. Given a repository of shapes, our approach learns a probabilistic graphical model that encodes semantic and geometric relationships among shape components. The probabilistic model is used to present components that are semantically and stylistically compatible with the 3D model that is being assembled. Our experiments indicate that the probabilistic model increases the relevance of presented components.

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References

[1]

Chaudhuri, S., and Koltun, V. 2010. Data-driven suggestions for creativity support in 3D modeling. In Proc. SIGGRAPH Asia, ACM.

Digital Library

[2]

Epic Games. 2011. Unreal Development Kit. http://www.udk.com.

[3]

Fisher, M., and Hanrahan, P. 2010. Context-based search for 3D models. In Proc. SIGGRAPH Asia, ACM.

Digital Library

[4]

Frey, B., and MacKay, D. 1997. A revolution: belief propagation in graphs with cycles. In Advances in Neural Information Processing Systems, 479--485.

Digital Library

[5]

Fung, R. M., and Chang, K.-C. 1990. Weighting and integrating evidence for stochastic simulation in Bayesian networks. In Proc. Conference on Uncertainty in Artificial Intelligence, North-Holland Publishing Co.

Digital Library

[6]

Funkhouser, T., Kazhdan, M., Shilane, P., Min, P., Kiefer, W., Tal, A., Rusinkiewicz, S., and Dobkin, D. 2004. Modeling by example. In Proc. SIGGRAPH, ACM.

Digital Library

[7]

Golovinskiy, A., and Funkhouser, T. 2009. Consistent segmentation of 3D models. Computers & Graphics 33, 3, 262--269.

Digital Library

[8]

Kalogerakis, E., Hertzmann, A., and Singh, K. 2010. Learning 3D mesh segmentation and labeling. In Proc. SIGGRAPH, ACM.

Digital Library

[9]

Koller, D., and Friedman, N. 2009. Probabilistic Graphical Models: Principles and Techniques. The MIT Press.

Digital Library

[10]

Kraevoy, V., Julius, D., and Sheffer, A. 2007. Model composition from interchangeable components. In Proc. Pacific Graphics, IEEE Computer Society.

Digital Library

[11]

Lee, J., and Funkhouser, T. 2008. Sketch-based search and composition of 3D models. In Proc. Eurographics Workshop on Sketch-Based Interfaces and Modeling.

Digital Library

[12]

Maxis Software. 2008. Spore. http://www.spore.com.

[13]

Merrell, P., Schkufza, E., and Koltun, V. 2010. Computer-generated residential building layouts. In Proc. SIGGRAPH Asia, ACM.

Digital Library

[14]

Mori, G., Belongie, S., and Malik, J. 2001. Shape contexts enable efficient retrieval of similar shapes. In Proc. IEEE Computer Vision and Pattern Recognition, vol. 1, 723--730.

[15]

Nealen, A., Sorkine, O., Alexa, M., and Cohen-Or, D. 2005. A sketch-based interface for detail-preserving mesh editing. In Proc. SIGGRAPH, ACM.

Digital Library

[16]

Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.

Digital Library

[17]

Podolak, J., Shilane, P., Golovinskiy, A., Rusinkiewicz, S., and Funkhouser, T. 2006. A planar-reflective symmetry transform for 3D shapes. In Proc. SIGGRAPH, ACM.

Digital Library

[18]

Schmidt, R., and Singh, K. 2010. Drag, drop, and clone: An interactive interface for surface composition. Tech. Rep. CSRG-611, Department of Computer Science, University of Toronto.

[19]

Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics, 2, 461--464.

[20]

Shapira, L., Shalom, S., Shamir, A., Zhang, R. H., and Cohen-Or, D. 2010. Contextual part analogies in 3D objects. International Journal of Computer Vision 89, 2-3, 309--326.

Digital Library

[21]

Sharf, A., Blumenkrants, M., Shamir, A., and Cohen-Or, D. 2006. SnapPaste: an interactive technique for easy mesh composition. Visual Computer 22, 9, 835--844.

Digital Library

[22]

Shen, C., O'Brien, J. F., and Shewchuk, J. R. 2004. Interpolating and approximating implicit surfaces from polygon soup. In Proc. SIGGRAPH, ACM.

Digital Library

[23]

Shin, H., and Igarashi, T. 2007. Magic canvas: interactive design of a 3D scene prototype from freehand sketches. In Proc. Graphics Interface.

Digital Library

[24]

Simari, P., Kalogerakis, E., and Singh, K. 2006. Folding meshes: hierarchical mesh segmentation based on planar symmetry. In Proc. Eurographics Symposium on Geometry Processing.

Digital Library

[25]

Sproull, R. F. 1990. Parts of the frontier are hard to move. Computer Graphics 24, 2, 9.

[26]

Talton, J. O., Gibson, D., Yang, L., Hanrahan, P., and Koltun, V. 2009. Exploratory modeling with collaborative design spaces. In Proc. SIGGRAPH Asia, ACM.

Digital Library

[27]

Torralba, A., Murphy, K. P., and Freeman, W. T. 2007. Sharing visual features for multiclass and multiview object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 5, 854--869.

Digital Library

[28]

Unity Technologies. 2011. Unity. http://unity3d.com.

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Wu DYang MLiu ZTu FLiu FCheng Z(2024)VRTree: Example‐Based 3D Interactive Tree Modeling in Virtual RealityComputer Graphics Forum10.1111/cgf.1525443:7Online publication date: 7-Nov-2024
https://doi.org/10.1111/cgf.15254
Guan Yvan Kaick O(2024)Diverse part synthesis for 3D shape creationComputer-Aided Design10.1016/j.cad.2024.103746175:COnline publication date: 25-Sep-2024
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Maesumi AGuerrero PAigerman NKim VFisher MChaudhuri SRitchie D(2023)Explorable Mesh Deformation Subspaces from Unstructured 3D Generative ModelsSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618192(1-11)Online publication date: 10-Dec-2023
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Index Terms

Probabilistic reasoning for assembly-based 3D modeling
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

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Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 30, Issue 4

July 2011

829 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2010324

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Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 July 2011

Published in TOG Volume 30, Issue 4

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Cited By

Wu DYang MLiu ZTu FLiu FCheng Z(2024)VRTree: Example‐Based 3D Interactive Tree Modeling in Virtual RealityComputer Graphics Forum10.1111/cgf.1525443:7Online publication date: 7-Nov-2024
https://doi.org/10.1111/cgf.15254
Guan Yvan Kaick O(2024)Diverse part synthesis for 3D shape creationComputer-Aided Design10.1016/j.cad.2024.103746175:COnline publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1016/j.cad.2024.103746
Maesumi AGuerrero PAigerman NKim VFisher MChaudhuri SRitchie D(2023)Explorable Mesh Deformation Subspaces from Unstructured 3D Generative ModelsSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618192(1-11)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.1145/3610548.3618192
Kim JMo KSung MWoo W(2023)Seg&Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00128(1226-1235)Online publication date: Jan-2023
https://doi.org/10.1109/WACV56688.2023.00128
Petrov DGadelha MMěch RKalogerakis E(2023)ANISE: Assembly-Based Neural Implicit Surface ReconstructionIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.326530630:8(4514-4526)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3265306
Li WChen XWang JChen B(2023)Patch-Based 3D Natural Scene Generation from a Single Example2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01608(16762-16772)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.01608
Xu XGuerrero PFisher MChaudhuri SRitchie D(2023)Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00827(8559-8567)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.00827
Zhang FFu QLiu YLi X(2023)Component-aware generative autoencoder for structure hybrid and shape completionGraphical Models10.1016/j.gmod.2023.101185129:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.gmod.2023.101185
Sun CYang YGuo HWang PTong XLiu YShum H(2023)Semi-supervised 3D shape segmentation with multilevel consistency and part substitutionComputational Visual Media10.1007/s41095-022-0281-99:2(229-247)Online publication date: 3-Jan-2023
https://doi.org/10.1007/s41095-022-0281-9
Liu XZhou SWu STan DYao R(2022)3D visualization model construction based on generative adversarial networksPeerJ Computer Science10.7717/peerj-cs.7688(e768)Online publication date: 29-Mar-2022
https://doi.org/10.7717/peerj-cs.768
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