Please use this identifier to cite or link to this item:
|Title: ||A study on local feature descriptors for point clouds|
|Authors: ||Rocha, Luís Cláudio Gouveia|
|Keywords: ||points clouds;local feature descriptors|
|Issue Date: ||24-Nov-2017|
|Publisher: ||Universidade Federal do Rio Grande do Norte|
|Citation: ||ROCHA, 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.|
|Portuguese Abstract: ||Point 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.|
|Other Identifiers: ||2013042960|
|Appears in Collections:||Ciência da Computação|
This item is licensed under a Creative Commons License