Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Proceedings of the 29th annual conference on Computer graphics and interactive techniques
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Proceedings of the 29th annual conference on Computer graphics and interactive techniques
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ACM SIGGRAPH 2008 papers
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Computer Graphics and Applications
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Computer Vision and Image Understanding
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International Journal of Computer Vision
3D human motion analysis framework for shape similarity and retrieval
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3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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This paper presents a performance evaluation of shape similarity metrics for 3D video sequences of people with unknown temporal correspondence. Performance of similarity measures is compared by evaluating Receiver Operator Characteristics for classification against ground-truth for a comprehensive database of synthetic 3D video sequences comprising animations of fourteen people performing twenty-eight motions. Static shape similarity metrics shape distribution, spin image, shape histogram and spherical harmonics are evaluated using optimal parameter settings for each approach. Shape histograms with volume sampling are found to consistently give the best performance for different people and motions. Static shape similarity is extended over time to eliminate the temporal ambiguity. Time-filtering of the static shape similarity together with two novel shape-flow descriptors are evaluated against temporal ground-truth. This evaluation demonstrates that shape-flow with a multi-frame alignment of motion sequences achieves the best performance, is stable for different people and motions, and overcome the ambiguity in static shape similarity. Time-filtering of the static shape histogram similarity measure with a fixed window size achieves marginally lower performance for linear motions with the same computational cost as static shape descriptors. Performance of the temporal shape descriptors is validated for real 3D video sequence of nine actors performing a variety of movements. Time-filtered shape histograms are shown to reliably identify frames from 3D video sequences with similar shape and motion for people with loose clothing and complex motion.