Style transfer is a recent field in the development of deep neural networks, which allows for the style from one image to be transferred onto another image. This has been well-researched for 2D images, but transferring style onto 3D reconstructed content can still be further developed. Being able to style a 3D reconstruction would allow users to recreate anything in the real world, such as a chair, with any style they see fit. Where other methods use texture-based approaches which often create low quality geometry and appearance, or use radiance fields which style a whole scene instead of just the 3D reconstructed object, we have developed a method which styles an implicit surface.
We achieve this by using Implicit Differentiable Renderer (IDR), which trains, using masked images as input, two neural networks that learn the geometry and appearance. Rendered views of the object are styled using 2D neural style transfer (NST) methods, and the style information is used to further train the appearance network to display the given style. With Masked deferred back-propagation we are able to optimize the appearance renderer, which is normally trained on only patches of the rendered image to save memory, while using style transfers designed for full-resolution images. We showcase different results from our method using different 3D reconstruction datasets and style images, and showcase how to implement a user-created dataset. We carry out extensive tests on what effects different parameters have on the final result. Comparing our results to similar 3D style methods demonstrates that our method performs equally well in achieving faithful style transfer, while having the benefits of creating high quality geometry and only styling the reconstructed surface.

Currently more than 4 billion people live in urban areas around the globe, a trend that is expected to be increased in the upcoming years. While urbanisation provides the space for innovation and new opportunities, in the meantime physical, technical and social challenges are rising and the cities’ vulnerability is increasing. A tool to tackle these issues are Computational Fluid Dynamics
(CFD) simulations, which can provide insight in various topics.

CFD simulations are valuable for modelling complex urban phenomena such as wind flow, microclimates and thermal comfort. A CFD requires as an input a 3D geometric dataset that represents objects in the urban environment which are most commonly buildings and then according to this input the air flow is simulated around it.

When creating geometries automatically for CFD simulations, several clean up tasks must be completed for them to be usable without any issues. One of the problems arising is related to the redundant faces shared between adjacent buildings, which have no purpose for outdoor flow simulations and cause complications when creating the mesh that is needed for the CFD. This
synthesis project focuses on addressing the aforementioned issue by removing the shared faces.

The ultimate goal of this project was to create an open-source product that can efficiently and in an automated way remove the adjacent faces between buildings. The benefits will be imminent during the meshing process, as we strive to reduce the time that consultancies spend fixing the input geometries before running a CFD simulation, along with an overall improved user experience.

This report is organised in four main sections. The first section is the general introduction of the issue that needs to resolved. The second section defines more in depth the problem and sets the research questions, in accordance to that, in the third section the research methodology is developed. In the fourth section the results of both methods are presented. The fifth sectionfocuses on a reflection of the project, while the sixth section presents the final conclusions. Finally, the seventh section contains the specifics of the project management itself.

The project was carried out in cooperation with Dassault Syst`emes and is developed in the context of the GEO1101 course in MSc Geomatics TU Delft. In addition to this report we have created a GitHub repository (https://github.com/Fabisser/facesBgone) that contains the source code of the two methods.