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Evaluation of 3D interest point detection techniques via human-generated ground truth

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Abstract

In this paper, we present an evaluation strategy based on human-generated ground truth to measure the performance of 3D interest point detection techniques. We provide quantitative evaluation measures that relate automatically detected interest points to human-marked points, which were collected through a web-based application. We give visual demonstrations and a discussion on the results of the subjective experiments. We use a voting-based method to construct ground truth for 3D models and propose three evaluation measures, namely False Positive and False Negative Errors, and Weighted Miss Error to compare interest point detection algorithms.

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Acknowledgements

We would like to thank Daniela Giorgi and AIM@SHAPE for the models from the Watertight Track of SHREC 2007, and Stanford Computer Graphics Laboratory for the models from The Stanford 3D Scanning Repository. We would like to thank Ivan Sipiran, Benjamin Bustos, Umberto Castellani, Ko Nishino, and Prabin Bariya for sharing their interest point detection codes.

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Authors and Affiliations

  1. Electrical-Electronics Engineering Department, Eskisehir Osmangazi University, Eskisehir, Turkey

    Helin Dutagaci

  2. National Institute of Standards and Technology (NIST), Gaithersburg, USA

    Chun Pan Cheung & Afzal Godil

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  1. Helin Dutagaci
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  2. Chun Pan Cheung
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  3. Afzal Godil
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Correspondence to Helin Dutagaci.

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Dutagaci, H., Cheung, C.P. & Godil, A. Evaluation of 3D interest point detection techniques via human-generated ground truth. Vis Comput 28, 901–917 (2012). https://doi.org/10.1007/s00371-012-0746-4

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