This paper proposes a method to generate surface and texture models from rigid objects captured with an RGB-D camera. The method integrates five stages: 1. Point cloud generation from RGB-D images; 2. Surface model generation; 3. Surface model refinement; 4. Texture generation and mapping; 5. Texture enhancement. The use of image processing algorithms for texture enhancement and the refinement of the surface models enables the improvement of the appearance of reconstructed models. The performed experimentation shows the results of the proposed method for five small textured objects. The appearance of reconstructed models was evaluated using a visual quality index; a sharper texture helps to improve such index.
This work presents a machine to locate a stereoscopic camera in different positions around an object to acquire a sequence of images that allows the reconstruction of such object through artificial vision algorithms. The GEMMA guide was used to define the modes of operation of the proposed machine. In addition, the mechanical and electronic elements that make up the machine and the programming logic for its control with PLC were also defined. It was demonstrated using a graphical interface that the mode of operation of the machine is carried out satisfactorily. Additionally, this work presents synthetic image results to represent a sequence of images acquired from different points of view considering different levels of elevation of the camera, showing the type of results obtained with the proposed machine.
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