Parameter Optimization for Surface Reconstruction

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s):
  • Rendering and Modeling
• Date: June 2025
• Date (Start): 2. April 2020
• Date (End): June 2025
• TU Wien Library:
• Diploma Examination: 16. June 2025
• First Supervisor:
  • Michael Wimmer
  • Philipp Erler
• Keywords: parameter optimization, deep learning, surface reconstruction

Abstract

In this thesis, we compare different parameter-optimization algorithms on the example of Screened Poisson Surface Reconstruction. To do this, we first implemented five state-of-the-art algorithms. GEIST is a graph-based algorithm that splits the parameter space into an `optimal' and a `non-optimal' set to select new configurations. Iterated F-Race places a normal distribution of selection probabilities on the best configurations of the last iteration and uses that to choose the next configurations. ParamILS uses iterative local search to select a better neighbor and find an optimum this way. PostSelection uses a shortened version of an algorithm to find promising candidates and a second, more detailed one to evaluate these. As a simple baseline we also implemented Brute-Force.

For all of these algorithms, we first conduct several tests to find a good configuration to run them with. After that, we test them on point clouds from two datasets. Each dataset contains each cloud in different qualities, so we are able to test varying input qualities as well as types. We show that each of the implemented algorithms is able to find better parameter configurations than the default Screened Poisson Surface Reconstruction configuration. In most cases, GEIST and PostSelection lead to the best results but also have the longest run times, while ParamILS and Iterated F-Race lead to good results in a far shorter time period. Brute-Force is not competitive when it comes to high-quality configurations, but still leads to an improvement over the default in most cases.

To summarize the results over different types and qualities, the default configuration yields acceptable but not ideal results for point clouds of smooth meshes with little noise and we suggest an alternative. If the surface is rougher, the importance weight of the points should ideally be set higher. If there is a lot of noise, this weight as well as the Octree depth should be reduced.

We discuss the advantages and disadvantages of each implemented algorithm and compare their results to recommend which one to use. We describe our implementations of each and quickly mention what work could be done to expand on this thesis. Finally, we give recommendations as to which configurations to use for different types of point clouds. For data with higher accuracy, depth and pointWeight should be higher than for data with lower accuracy. If the topology of the object is very complex, $pointWeight$ is best set very high in comparison to simpler point clouds. We find that for most cases, IF-Race is the best compromise to use between speed and resulting quality of reconstruction. If time is of no concern, GEIST is an alternative that yields high-quality results.

Repository: https://github.com/thefloff/ppsurf_param_opt

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BibTeX

@mastersthesis{steinschorn-2025-parameter,
  title =      "Parameter Optimization for Surface Reconstruction",
  author =     "Florian Steinschorn",
  year =       "2025",
  abstract =   "In this thesis, we compare different parameter-optimization
               algorithms on the example of Screened Poisson Surface
               Reconstruction. To do this, we first implemented five
               state-of-the-art algorithms. GEIST is a graph-based
               algorithm that splits the parameter space into an `optimal'
               and a `non-optimal' set to select new configurations.
               Iterated F-Race places a normal distribution of selection
               probabilities on the best configurations of the last
               iteration and uses that to choose the next configurations.
               ParamILS uses iterative local search to select a better
               neighbor and find an optimum this way. PostSelection uses a
               shortened version of an algorithm to find promising
               candidates and a second, more detailed one to evaluate
               these. As a simple baseline we also implemented Brute-Force.
                For all of these algorithms, we first conduct several tests
               to find a good configuration to run them with. After that,
               we test them on point clouds from two datasets. Each dataset
               contains each cloud in different qualities, so we are able
               to test varying input qualities as well as types. We show
               that each of the implemented algorithms is able to find
               better parameter configurations than the default Screened
               Poisson Surface Reconstruction configuration. In most cases,
               GEIST and PostSelection lead to the best results but also
               have the longest run times, while ParamILS and Iterated
               F-Race lead to good results in a far shorter time period.
               Brute-Force is not competitive when it comes to high-quality
               configurations, but still leads to an improvement over the
               default in most cases.  To summarize the results over
               different types and qualities, the default configuration
               yields acceptable but not ideal results for point clouds of
               smooth meshes with little noise and we suggest an
               alternative. If the surface is rougher, the importance
               weight of the points should ideally be set higher. If there
               is a lot of noise, this weight as well as the Octree depth
               should be reduced.  We discuss the advantages and
               disadvantages of each implemented algorithm and compare
               their results to recommend which one to use. We describe our
               implementations of each and quickly mention what work could
               be done to expand on this thesis. Finally, we give
               recommendations as to which configurations to use for
               different types of point clouds. For data with higher
               accuracy, depth and pointWeight should be higher than for
               data with lower accuracy. If the topology of the object is
               very complex, $pointWeight$ is best set very high in
               comparison to simpler point clouds. We find that for most
               cases, IF-Race is the best compromise to use between speed
               and resulting quality of reconstruction. If time is of no
               concern, GEIST is an alternative that yields high-quality
               results.  Repository:
               https://github.com/thefloff/ppsurf_param_opt",
  month =      jun,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien",
  keywords =   "parameter optimization, deep learning, surface
               reconstruction",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/steinschorn-2025-parameter/",
}