A study on local feature descriptors for point clouds

dc.contributor.advisorCarvalho, Bruno Motta de
dc.contributor.authorRocha, Luís Cláudio Gouveia
dc.contributor.referees1Carvalho, Bruno Motta de
dc.contributor.referees2Gomes, Rafael Beserra
dc.contributor.referees3Angeles, Edgar Garduño
dc.date.accessioned2017-12-15T12:48:57Z
dc.date.accessioned2021-09-20T11:46:43Z
dc.date.available2017-12-15T12:48:57Z
dc.date.available2021-09-20T11:46:43Z
dc.date.issued2017-11-24
dc.description.resumoPoint clouds are a way of representing 3D data which became very popular due to the rise of low-cost 3D sensors on the market whose output data is represented as a point cloud. Given it low-cost, these sensors have been used used in many different fields, such as games or robotics. In many of these applications, recognizing patterns inside big, unorganized clouds is a fundamental task which is often solved using local feature descriptors, which are a way of encoding information local to a region inside a bigger cloud. Nevertheless, pattern recognition using local feature descriptors is a hard task, whose results nowadays are far from satisfactory (in terms of quality and speed) for most of the non-synthetic scenarios, which motivates the development of new descriptors. As a first series of experiments towards both fast descriptors and descriptors robust to high clutter and occlusion, we develop five descriptors, two of them being simplified (thus faster) versions of existing state-of-the-art techniques, one a totally novel approach to discrete descriptors and two being extensions and adaptations of existing descriptors. Our tests show that although our proposals perform poorly when compared to the state-of-the-art, their simplistic design is enough to achieve reasonable results and perform close to some existing techniques, motivating us to keep improving these results. As a byproduct of our work, we produced a benchmark platform which is open for public usage and improvement, aiming to encourage the standardization of tests with feature descriptors.pr_BR
dc.identifier2013042960pr_BR
dc.identifier.citationROCHA, Luís Cláudio Gouveia. A study on local feature descriptors for point clouds. 2017. 76 f. TCC (Graduação) - Curso de Ciência da Computação, Departamento de Informática e Matemática Aplicada, Universidade Federal do Rio Grande do Norte, Natal, 2017.pr_BR
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/34187
dc.languageen_USpr_BR
dc.publisherUniversidade Federal do Rio Grande do Nortepr_BR
dc.publisher.countryBrasilpr_BR
dc.publisher.departmentCiência da Computaçãopr_BR
dc.publisher.initialsUFRNpr_BR
dc.rightsopenAccesspr_BR
dc.subjectpoints cloudspr_BR
dc.subjectlocal feature descriptorspr_BR
dc.subject.cnpqCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::PROCESSAMENTO GRAFICO (GRAPHICS)pr_BR
dc.titleA study on local feature descriptors for point cloudspr_BR
dc.typebachelorThesispr_BR

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