Journal Article10.1145/2601097.2601111
Functional map networks for analyzing and exploring large shape collections
Qixing Huang,Fan Wang,Leonidas J. Guibas +2 more
- 27 Jul 2014
- Vol. 33, Iss: 4, pp 36
TL;DR: This paper shows how to rigorously formulate the consistency constraint in the functional map setting, and leads to a powerful tool for computing consistent functional maps, and also for discovering shared structures, such as meaningful shape parts.
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Abstract: The construction of networks of maps among shapes in a collection enables a variety of applications in data-driven geometry processing. A key task in network construction is to make the maps consistent with each other. This consistency constraint, when properly defined, leads not only to a concise representation of such networks, but more importantly, it serves as a strong regularizer for correcting and improving noisy initial maps computed between pairs of shapes in isolation. Up-to-now, however, the consistency constraint has only been fully formulated for point-based maps or for shape collections that are fully similar. In this paper, we introduce a framework for computing consistent functional maps within heterogeneous shape collections. In such collections not all shapes share the same structure --- different types of shared structure may be present within different (but possibly overlapping) sub-collections. Unlike point-based maps, functional maps can encode similarities at multiple levels of detail (points or parts), and thus are particularly suitable for coping with such diversity within a shape collection. We show how to rigorously formulate the consistency constraint in the functional map setting. The formulation leads to a powerful tool for computing consistent functional maps, and also for discovering shared structures, such as meaningful shape parts. We also show how to adapt the procedure for handling very large-scale shape collections. Experimental results on benchmark datasets show that the proposed framework significantly improves upon state-of-the-art data-driven techniques. We demonstrate the usefulness of the framework in shape co-segmentation and various shape exploration tasks.
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Citations
A scalable active framework for region annotation in 3D shape collections
Li Yi,Vladimir G. Kim,Duygu Ceylan,I-Chao Shen,Mengyan Yan,Hao Su,Cewu Lu,Qixing Huang,Alla Sheffer,Leonidas J. Guibas +9 more
- 11 Nov 2016
TL;DR: This work proposes a novel active learning method capable of enriching massive geometric datasets with accurate semantic region annotations, and demonstrates that incorporating verification of all produced labelings within this unified objective improves both accuracy and efficiency of the active learning procedure.
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SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
Li Yi,Hao Su,Xingwen Guo,Leonidas J. Guibas +3 more
- 01 Jul 2017
TL;DR: SyncSpecCNN as mentioned in this paper proposes a spectral convolutional neural network for 3D shape part segmentation and keypoint prediction, which enables weight sharing by parametrizing kernels in the spectral domain spanned by graph Laplacian eigenbases.
GRASS: generative recursive autoencoders for shape structures
TL;DR: A novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures, is introduced and it is demonstrated that without supervision, the network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.
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3D Shape Segmentation with Projective Convolutional Networks
Evangelos Kalogerakis,Melinos Averkiou,Subhransu Maji,Siddhartha Chaudhuri +3 more
- 01 Jul 2017
TL;DR: This paper introduces a deep architecture for segmenting 3D objects into their labeled semantic parts that significantly outperforms the existing state-of-the-art methods in the currently largest segmentation benchmark (ShapeNet).
•Posted Content
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation.
Mingyang Jiang,Yiran Wu,Cewu Lu +2 more
TL;DR: Inspired by the outstanding 2D shape descriptor SIFT, a module called PointSIFT is designed that encodes information of different orientations and is adaptive to scale of shape, which outperforms state-of-the-art method on standard benchmark datasets.
458
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