Graph convolutional networks (GCNs) showed promising results in learning from point cloud data. Applications of GCNs include point cloud classification, point cloud segmentation, point cloud upsampling, and more. Recently, the introduction of Deep Graph Convolutional Networks (DeepGCNs) allowed GCNs to go deeper, and thus resulted in better graph learning while avoiding the vanishing gradient problem in GCNs. By adapting impactful methods from convolutional neural networks (CNNs) such as residual connections, dense connections, and dilated convolutions, DeepGCNs allowed GCNs to learn better from non-Euclidean data. In addition, deep learning methods proved very effective in the task of point cloud upsampling. Unlike traditional optimization-based methods, deep learning-based methods to point cloud upsampling does not rely on priors nor hand-crafted features to learn how to upsample point clouds. In this thesis, I discuss the impact and show the performance results of DeepGCNs in the task of point cloud part segmentation on PartNet dataset. I also illustrate the significance of using GCNs as upsampling modules in the task of point cloud upsampling by introducing two novel upsampling modules: Multi-branch GCN and Clone GCN. I show quantitatively and qualitatively the performance results of our novel and versatile upsampling modules when evaluated on a new proposed standardized dataset: PU600, which is the largest and most diverse point cloud upsampling dataset currently in the literature.

Online house building tools are robust instruments that have transformed the approach to home planning and design. The significance of 3D models on online house building and design platforms lies in their ability to elevate the user experience, enhance design precision, and foster collaboration. The creation of 3D house models is a demanding and detail-oriented undertaking that requires significant dedication and expertise. This thesis plans to investigate possibilities of implementing AI with a limited amount of data, to help streamline the manual process of working on 3D models for use in online house building tools. Multiple problems arouse related to issues with the acquired house mesh data, issues with the chosen AI models and hardware issues.  The problems during the different stages of work made any meaningful analyses hard but some evaluations could still be extracted. Time was a central aspect in that the time taken to train the AI models greatly correlated with the amount of data used for said training. It was also made apparent that there was a great need for more data if the AI was to be able to be trained on a dataset made from 3D house meshes. If this type of project was to be picked up in the future, a recommendation would be to not go at it alone as the time needed to perform the different steps should not be underestimated.