{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pointnet-deep-learning-on-point-sets-for-3d","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","arxiv_id":"1612.00593","date":"2016-12-02","proceeding":"CVPR 2017 7","authors":["Charles R. Qi","Hao Su","Kaichun Mo","Leonidas J. Guibas"],"abstract":"Point cloud is an important type of geometric data structure. Due to its\nirregular format, most researchers transform such data to regular 3D voxel\ngrids or collections of images. This, however, renders data unnecessarily\nvoluminous and causes issues. In this paper, we design a novel type of neural\nnetwork that directly consumes point clouds and well respects the permutation\ninvariance of points in the input. Our network, named PointNet, provides a\nunified architecture for applications ranging from object classification, part\nsegmentation, to scene semantic parsing. Though simple, PointNet is highly\nefficient and effective. Empirically, it shows strong performance on par or\neven better than state of the art. Theoretically, we provide analysis towards\nunderstanding of what the network has learnt and why the network is robust with\nrespect to input perturbation and corruption.","url_abs":"http://arxiv.org/abs/1612.00593v2","url_pdf":"http://arxiv.org/pdf/1612.00593v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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