Defesa de Tesa de Doutorado: Advancing 3D Manipulation in Virtual Reality: Design and Evaluation of High-Precision Techniques and a Comprehensive Taxonomy

Orientadores:
Jauvane Cavalcante de Oliveira - Laboratório Nacional de Computação Científica - LNCC
Douglas Andrew Bowman

Banca Examinadora:
Jauvane Cavalcante de Oliveira - Laboratório Nacional de Computação Científica - LNCC (presidente)
Antônio Tadeu Azevedo Gomes - Laboratório Nacional de Computação Científica - LNCC
Márcio Sarroglia Pinho - FI/PUCRS
Rosa Maria Esteves Moreira da Costa - IME – Departamento de Informática e Ciência da Computação, Universidade do Estado de Rio de Janeiro – UERJ
Liliane dos Santos Machado - Universidade Federal da Paraíba

Suplentes:
Fabio Andre Machado Porto - Laboratório Nacional de Computação Científica - LNCC
Alberto Barbosa Raposo - Pontifícia Universidade Católica do Rio de Janeiro - PUC-RIO

Resumo:

Precise 3D manipulation in virtual reality (VR) is essential for aligning virtual objects effectively. However, the limitations of state-of-the-art VR manipulation techniques become apparent when high levels of precision are required. These include the unnaturalness caused by scaled rotations and the increased time due to the separation of degrees of freedom (DoF) in complex tasks. Moreover, existing taxonomies for classifying these techniques do not comprehensively cover the entire design space of 3D manipulation methods. To bridge these gaps, we developed a new taxonomy for the classification of 3D manipulation techniques, enhanced by a visual representation tool. This tool aids in analyzing current techniques and identifying areas for improvement, thus facilitating the design and evaluation of new methods. Our taxonomy is further validated by extensively re presenting various existing techniques from the literature. Building on these insights and the limitations identified, we introduce two novel techniques: AMP-IT, which offers direct manipulation with an adaptive scaled mapping for implicit DoF separation, and WISDOM, which combines Simple Virtual Hand and scaled indirect manipulation with explicit DoF separation. In a controlled experiment, we compared these techniques against both baseline and state-of-the-art manipulation techniques. The results indicate that WISDOM and AMP-IT have significant advantages over current best-practice techniques in terms of task performance, usability, and user preference.