Print Email Facebook Twitter Procedural Tree Generation Title Procedural Tree Generation: Compressing 3D tree for faster rendering Author Manda, Sebastian (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Eisemann, E. (mentor) Kellnhofer, P. (mentor) Uzolas, L. (mentor) Reinders, M.J.T. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2024-02-01 Abstract Trees are essential components of both real and digital environments. Therefore, it is important to have 3D models of trees that are of high quality and computationally efficient. One way to achieve this is by compressing a high-quality model using billboard rendering, which involves partitioning the tree into multiple planes to produce a similar result to the original. Our study explores the compression of 3D models using an optimization loop and adapting billboard rendering techniques. We use computer vision primitives to render basic models, which we then optimize by adjusting the texture to resemble the original tree. The models consist of multiple upright planes that are rotated around the central vertical axis of the original tree. We use different optimization functions, such as L1 and L2 losses, to determine the best approach. We can improve the initial models by bounding the billboards and limiting their heights and widths to that of the trees. Additionally, we can use double-sided textures for the billboards to allow more flexibility for optimizing different species of trees. However, optimizing multiple tree types performs differently for each species, leading to improvements that only benefit certain trees in specific scenarios. Using quantitative metrics, we determined which models perform best and how similar they are to the original after training. We found that our compressed models generally resemble the original while having only a fraction of the original size. Subject Computer Graphics3D Model CompressionRenderingLoss Optimization To reference this document use: http://resolver.tudelft.nl/uuid:7ca6216f-2636-4c9c-85d3-f381b956870f Part of collection Student theses Document type bachelor thesis Rights © 2024 Sebastian Manda Files PDF sebastian_manda_final_paper.pdf 1.76 MB Close viewer /islandora/object/uuid:7ca6216f-2636-4c9c-85d3-f381b956870f/datastream/OBJ/view