{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:12:49Z","timestamp":1769584369158,"version":"3.49.0"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,6,5]],"date-time":"2022-06-05T00:00:00Z","timestamp":1654387200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,5]],"date-time":"2022-06-05T00:00:00Z","timestamp":1654387200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,5]]},"DOI":"10.1109\/iv51971.2022.9827116","type":"proceedings-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T19:33:28Z","timestamp":1658259208000},"page":"594-599","source":"Crossref","is-referenced-by-count":5,"title":["What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation"],"prefix":"10.1109","author":[{"given":"Frederik","family":"Hasecke","sequence":"first","affiliation":[{"name":"Bergische Universität Wuppertal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Alsfasser","sequence":"additional","affiliation":[{"name":"Aptiv Services Deutschland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anton","family":"Kummert","sequence":"additional","affiliation":[{"name":"Bergische Universität Wuppertal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref30","article-title":"Cylindrical and asymmetrical 3d convolution networks for lidar segmentation","author":"zhu","year":"2020","journal-title":"arXiv preprint arXiv 2011 10068"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9564842"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"5432","DOI":"10.1109\/LRA.2020.3007440","article-title":"3d-mininet: Learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation","volume":"5","author":"inigo","year":"2020","journal-title":"IEEE l of Robotics and Automation"},{"key":"ref12","first-page":"73","article-title":"Data mining for direct marketing: Problems and solutions","volume":"98","author":"ling","year":"1998","journal-title":"KDD"},{"key":"ref13","article-title":"Amvnet: Assertion-based multi-view fusion network for lidar semantic segmentation","author":"liong","year":"2020","journal-title":"2012 arXiv preprint arXiv"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/3DV53792.2021.00022"},{"key":"ref16","article-title":"Omnia faster r-cnn: Detection in the wild through dataset merging and soft distillation","author":"rame","year":"2018","journal-title":"arXiv preprint arXiv 1812 02588"},{"key":"ref17","article-title":"The elephant in the room","author":"rosenfeld","year":"2018","journal-title":"arXiv preprint arXiv 1808 03578"},{"key":"ref18","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00651"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01299"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref27","article-title":"Pointcutmix: Regularization strategy for point cloud classification","author":"zhang","year":"2021","journal-title":"arXiv preprint arXiv 2101 06286"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00939"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-64559-5_16"},{"key":"ref29","article-title":"Vpfnet: Improving 3d object detection with virtual point based lidar and stereo data fusion","author":"zhu","year":"2021","journal-title":"arXiv preprint arXiv 2111 14382"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_20"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00291"},{"key":"ref7","article-title":"Improved regularization of convolutional neural networks with cutout","author":"devries","year":"2017","journal-title":"arXiv preprint arXiv 1708 04552"},{"key":"ref2","first-page":"102","article-title":"Multimodal 3d object detection from simulated pretraining","author":"\u00e5smund","year":"2019","journal-title":"Symposium of the Norwegian AI Society"},{"key":"ref9","first-page":"177","article-title":"Comparing biases for minimal network construction with back-propagation","volume":"1","author":"hanson","year":"1988","journal-title":"Advances in neural information processing systems"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2557043"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304631"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01572"},{"key":"ref21","first-page":"875","article-title":"Generalization by weight-elimination with application to forecasting","author":"weigend","year":"1991","journal-title":"Advances in neural information processing systems"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"3101","DOI":"10.1609\/aaai.v35i4.16419","article-title":"Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion","volume":"35","author":"yan","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.3390\/s18103337","article-title":"Second: Sparsely embedded convolutional detection","volume":"18","author":"yan","year":"2018","journal-title":"SENSORS"},{"key":"ref26","article-title":"mixup: Beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"International Conference on Learning Representations"},{"key":"ref25","first-page":"6023","article-title":"Cutmix: Regularization strategy to train strong classifiers with localizable features","author":"yun","year":"2019","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"}],"event":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","location":"Aachen, Germany","start":{"date-parts":[[2022,6,4]]},"end":{"date-parts":[[2022,6,9]]}},"container-title":["2022 IEEE Intelligent Vehicles Symposium (IV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9826996\/9826997\/09827116.pdf?arnumber=9827116","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,12]],"date-time":"2023-02-12T00:48:56Z","timestamp":1676162936000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9827116\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,5]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/iv51971.2022.9827116","relation":{},"subject":[],"published":{"date-parts":[[2022,6,5]]}}}