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A Performance Evaluation of Volumetric 3D Interest Point Detectors

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Abstract

This paper presents the first performance evaluation of interest points on scalar volumetric data. Such data encodes 3D shape, a fundamental property of objects. The use of another such property, texture (i.e. 2D surface colouration), or appearance, for object detection, recognition and registration has been well studied; 3D shape less so. However, the increasing prevalence of 3D shape acquisition techniques and the diminishing returns to be had from appearance alone have seen a surge in 3D shape-based methods. In this work, we investigate the performance of several state of the art interest points detectors in volumetric data, in terms of repeatability, number and nature of interest points. Such methods form the first step in many shape-based applications. Our detailed comparison, with both quantitative and qualitative measures on synthetic and real 3D data, both point-based and volumetric, aids readers in selecting a method suitable for their application.

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Notes

  1. Covariant characteristics, often (inaccurately) referred to as invariant characteristics, undergo the same transformation as the data. We prefer “covariant” in order to distinguish truly invariant characteristics.

  2. When referring to interest points in the context of methodology, we include image features such as corners, lines, edges and blobs.

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Authors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Tsz-Ho Yu & Roberto Cipolla

  2. Toshiba Research Europe Ltd., Cambridge, UK

    Oliver J. Woodford

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  1. Tsz-Ho Yu
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  2. Oliver J. Woodford
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Correspondence to Tsz-Ho Yu.

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Yu, TH., Woodford, O.J. & Cipolla, R. A Performance Evaluation of Volumetric 3D Interest Point Detectors. Int J Comput Vis 102, 180–197 (2013). https://doi.org/10.1007/s11263-012-0563-2

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