Abstract
During the last years a wide range of algorithms and devices have been made available to easily acquire range images. The increasing abundance of depth data boosts the need for reliable and unsupervised analysis techniques, spanning from part registration to automated segmentation. In this context, we focus on the recognition of known objects in cluttered and incomplete 3D scans. Locating and fitting a model to a scene are very important tasks in many scenarios such as industrial inspection, scene understanding, medical imaging and even gaming. For this reason, these problems have been addressed extensively in the literature. Several of the proposed methods adopt local descriptor-based approaches, while a number of hurdles still hinder the use of global techniques. In this paper we offer a different perspective on the topic: We adopt an evolutionary selection algorithm that seeks global agreement among surface points, while operating at a local level. The approach effectively extends the scope of local descriptors by actively selecting correspondences that satisfy global consistency constraints, allowing us to attack a more challenging scenario where model and scene have different, unknown scales. This leads to a novel and very effective pipeline for 3D object recognition, which is validated with an extensive set of experiments and comparisons with recent techniques at the state of the art.
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Notes
The dataset together with ground-truth information can be downloaded at http://www.dsi.unive.it/~rodola/data.html.
Matlab code for infection-immunization dynamics is available at http://www.dsi.unive.it/~rodola/sw.html.
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Acknowledgements
We wish to thank Dr. Samuele Salti for contributing code to compute SHOT descriptors, Prof. Ajmal S. Mian and Dr. Prabin Bariya for providing us with the experimental results used to compare our approach with their methods. We acknowledge the financial support of the Future and Emerging Technology (FET) Programme within the Seventh Framework Programme for Research of the European Commission, under FET-Open project SIMBAD Grant No. 213250.
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Rodolà, E., Albarelli, A., Bergamasco, F. et al. A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes. Int J Comput Vis 102, 129–145 (2013). https://doi.org/10.1007/s11263-012-0568-x
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DOI: https://doi.org/10.1007/s11263-012-0568-x