3D reconstruction from multiple RGB-D images with different perspectives

• Autor(a) principal: Mário André Pinto Ferraz de Aguiar
• Data de Publicação: 2015
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/89542
• topic: Engenharia electrotécnica, electrónica e informática; Electrical engineering, Electronic engineering, Information engineering

3D model reconstruction can be a useful tool for multiple purposes. Some examples are modeling a person or objects for an animation, in robotics, modeling spaces for exploration or, for clinical purposes, modeling patients over time to keep a history of the patient's body. The reconstruction process is constituted by the captures of the object to be reconstructed, the conversion of these captures to point clouds and the registration of each point cloud to achieve the 3D model. The implemented methodology for the registration process was as much general as possible, to be usable for the multiple purposes discussed above, with a special focus on non-rigid objects. This focus comes from the need to reconstruct high quality 3D models, of patients treated for breast cancer, for the evaluation of the aesthetic outcome. With the non-rigid algorithms the reconstruction process is more robust to small movements during the captures. The sensor used for the captures was the Microsoft Kinect, due to the possibility of obtaining both color (RGB) and depth images, called RGB-D images. With this type of data the final 3D model can be textured, which is an advantage for many cases. The other main reason for this choice was the fact that Microsoft Kinect is a low-cost equipment, thereby becoming an alternative to expensive systems available in the market. The main achieved objectives were the reconstruction of 3D models with good quality from noisy captures, using a low cost sensor. The registration of point clouds without knowing the sensor's pose, allowing the free movement of the sensor around the objects. Finally the registration of point clouds with small deformations between them, where the conventional rigid registration algorithms could not be used.