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Relief analysis and extraction
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Partial intrinsic reflectional symmetry of 3D shapes
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Multi-objective shape segmentation and labeling
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Discrete scale axis representations for 3D geometry
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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Reality-based 3D modeling, segmentation and web-based visualization
EuroMed'10 Proceedings of the Third international conference on Digital heritage
Characterizing 3D shapes using fractal dimension
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
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SMI 2011: Full Paper: An approach to automated decomposition of volumetric mesh
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Semi-supervised segmentation of 3D surfaces using a weighted graph representation
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Cover geometry design using multiple convex hulls
Computer-Aided Design
Semantic 3D Media and Content: Semantics and 3D media: Current issues and perspectives
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Joint shape segmentation with linear programming
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Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering
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A robust approach to multi-feature based mesh segmentation using adaptive density estimation
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Schelling points on 3D surface meshes
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Micro perceptual human computation for visual tasks
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Chopper: partitioning models into 3D-printable parts
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Fast human classification of 3D object benchmarks
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This paper describes a benchmark for evaluation of 3D mesh segmentation salgorithms. The benchmark comprises a data set with 4,300 manually generated segmentations for 380 surface meshes of 19 different object categories, and it includes software for analyzing 11 geometric properties of segmentations and producing 4 quantitative metrics for comparison of segmentations. The paper investigates the design decisions made in building the benchmark, analyzes properties of human-generated and computer-generated segmentations, and provides quantitative comparisons of 7 recently published mesh segmentation algorithms. Our results suggest that people are remarkably consistent in the way that they segment most 3D surface meshes, that no one automatic segmentation algorithm is better than the others for all types of objects, and that algorithms based on non-local shape features seem to produce segmentations that most closely resemble ones made by humans.