Parameter Optimization at the example of Surface Reconstruction (DAAV)

Many operations in the field of computer science, or using a computer in general, require the selection of parameters or configurations with which to run. The outcome of the operation often heavily depends on the quality of the selected parameters. As an example, Screened Poisson Surface Reconstruction (SPSR), as implemented by Meshlab, can use different depths, point-weights or samples per node, that all heavily change the resulting mesh. A bad selection of these parameters leads to a worse or failed reconstruction or can cause the process to take far more time than for a similar result with different parameters.
In most cases, sensible default values exist or can be selected by an expert, but finding an optimum is difficult. For small parameter-spaces or fast operations it might be possible to fully map all parameter combinations to their output, but more complex problems and higher numbers of possible parameters make such an approach infeasible. The aim of this thesis is to find an efficient solution to finding good parameters for any parameterized operation. This is to be done in two different ways. For general operations, this solution is required to produce parameter configurations that improve the performance of the operation it is applied to. This will be mainly tested on SPSR. Additionally, for Points2Surf, a patch-based learning framework that produces surfaces from point clouds resulting from 3D scans, a second approach is to be tested. Since Points2Surf has a not insignificant runtime, minimizing the number of runs necessary to find an optimum will be the main goal. To achieve this, this thesis will attempt to find optimal parameters for Points2Surf without having to run the full optimisation strategy each time. This could be achieved by training a neural network on many point clouds and their precomputed optimal parameters.