Local pose estimation of feature points for object based augmented reality.

• Ano de defesa: 2016
• Autor(a) principal: Tokunaga, Daniel Makoto
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Augmented reality; Computação gráfica; Computer vision; Detecção de poses. Poses locais; Pose estimation; Processamento de imagens; Realidade aumentada; Realidade virtual; Visão computacional
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/3/3141/tde-22092016-110832/

Usage of real objects as links between real and virtual information is one key aspect in augmented reality. A central issue to achieve this link is the estimation of the visuospatial information of the observed object, or in other words, estimating the object pose. Different objects can have different behaviors when used for interaction. This not only encompasses changes in position, but also folding or deformations. Traditional researches in the area solve those pose estimation problems using different approaches, depending on the type of the object. Additionally, some researches are based only on positional information of observed feature points, simplifying the object information. In this work, we explore the pose estimation of different objects by gathering more information from the observed feature points, and obtaining the local poses of such points, which are not explored in other researches. We apply this local pose estimation idea in two different capturing scenarios, reaching two novel approaches of pose estimation: one based on RGB-D cameras, and another based on RGB and machine learning methods. In the RGB-D based approach, we use the feature point orientation and near surface to obtain its normal; then, find the local 6 degrees-of-freedom (DoF) pose. This approach gives us not only the rigid object pose, but also the approximated pose of deformed objects. On the other hand, our RGB based approach explores machine learning with local appearance changes. Unlike other RGB based works, we replace the complex non-linear systems solvers with a fast and robust method, reaching local rotation of the observed feature points, as well as, full 6 DoF rigid object pose with dramatically lower real-time calculation demands. Both approaches show us that gathering local poses can bring information for the pose estimation of different types of objects.