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Due to its outstanding performance, there have been efforts to apply it in more challenging scenarios, for example, 3D data processing. This article surveys methods applying deep learning on 3D data and provides a classification based on how they exploit them. From the results of the examined works, we conclude that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation. Therefore, larger-scale datasets and increased resolutions are required.<\/jats:p>","DOI":"10.1145\/3042064","type":"journal-article","created":{"date-parts":[[2017,4,6]],"date-time":"2017-04-06T12:50:07Z","timestamp":1491483007000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":258,"title":["Deep Learning Advances in Computer Vision with 3D Data"],"prefix":"10.1145","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3876-8405","authenticated-orcid":false,"given":"Anastasia","family":"Ioannidou","sequence":"first","affiliation":[{"name":"Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece"}]},{"given":"Elisavet","family":"Chatzilari","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece"}]},{"given":"Spiros","family":"Nikolopoulos","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece"}]},{"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece"}]}],"member":"320","published-online":{"date-parts":[[2017,4,6]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"M. 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