Pereira, António Manuel de JesusRodrigues, Nuno Carlos SousaGrilo, Carlos Fernando de AlmeidaCarreira, Daniel Soares2024-12-042024-11-15http://hdl.handle.net/10400.8/10281The manufacturing industry is undergoing a significant transformation with the onset of the Fourth Industrial Revolution. A key aspect of this shift is the integration of advanced technologies, such as smart sensors and automation, into production processes. Within this context, 3D cameras have become invaluable, enabling manufacturers to capture precise surface measurements of the products. In response, the computer vision community has begun exploring new methods to combine depth information with color data, enhancing existing solutions for classification and design generation. By harnessing these advancements, manufacturers can streamline production lines, reduce waste, and elevate product quality. This dissertation presents two key innovations for classification and generation tasks: (1) a novel branched Convolutional Neural Network (CNN), which achieves stateof- the-art performance in RGB-Depth (RGB-D) image classification, and (2) a novel branched Generative Adversarial Network (GAN), inspired by the same branched architecture, that delivers state-of-the-art results on the Stanford Cars dataset. The core idea of this branched approach is to specialize each branch to handle a specific modality. In the experiments, the classification performance improved by approximately 1%, while achieving nearly three times the speed of the next best method. For image generation, results varied depending on the dataset. On the Stanford Cars benchmark, the model showed slight improvements in image quality and better diversity.engBranched CNNBranched GANImage ClassificationImage GenerationImage ManipulationRGB-D Fusionmaster thesis203745973