Tool: Mesh2PPM 1.0.0 (2022-2024)

Subtitle: Code to predict local BezierPPM parameters from a pinna geometry
Cite as: Perfler, F., Pausch, F., Holighaus, N., and Majdak, P. (2024). "Mesh2PPM 1.0.0", The SONICOM Ecosystem: Tool #4. URL: https://ecosystem.sonicom.eu/tools/4. Copy Citation to Clipboard

The File

  • File Name: Mesh2PPM-Evaluation-Paper-Local-Parameters.zip
  • Download Link: Mesh2PPM-Evaluation-Paper-Local-Parameters.zip Copy to Clipboard
  • File Size: 78109 bytes = 76.28 kbytes

    Metadata

    Creators:

    • Perfler, Felix ORCID: 0009-0004-8738-8462 , Acoustics Research Institute ROR: 04jd9ff79
    • Pausch, Florian ORCID: 0000-0003-2728-3170 , Acoustics Research Institute ROR: 04jd9ff79
    • Holighaus, Nicki ORCID: 0000-0003-3837-2865 , Acoustics Research Institute ROR: 04jd9ff79
    • Majdak, Piotr ORCID: 0000-0003-1511-6164 , Acoustics Research Institute ROR: 04jd9ff79

    Publishers:

    • The SONICOM Ecosystem of the Austrian Academy of Sciences ROR: 03anc3s24

    Rightsholders:

    • Acoustics Research Institute ROR: 04jd9ff79

    Keywords:

    • Physically based modeling
    • Computer vision
    • Neural network
    • Vision Transformer
    • Supervised learning
    • Regression

    Relations:

    Other:

    • DOI: not assigned yet
    • Uploaded by: Florian Pausch
    • Date (created): 2025-06-29 21:55:14 (GMT)
    • Date (updated): 2025-12-02 09:38:22 (GMT)
    • Production Year: 2022-2024
    • Resource Type: Model
    • Rights: EUPL-1.2: European Union Public Licence version 1.2
    • Subject Areas: Life Science , Other SONICOM Ecosystem
    • General Description: Tool Mesh2PPM 1.0.0 takes synthetic pinna geometries generated with Tool PyBezierPPM 3.0 as input to predict the local parameters of Tool BezierPPM 3.0.
    • Methods: Tool Mesh2PPM 1.0.0 consists of a rendering stage and a deep neural network (DNN). The synthetic pinna geometries at the input are rendered with Tool PyBezierPPM 3.0 as multi-view grey-scale pinna images and optionally as depth images, i.e., MVPD pinna images. The DNN uses these MVPD pinna images as input and predicts the local parameters of Tool BezierPPM 3.0. The Database Mesh2PPM 1.0.0: Weights provides weights for various DNN instances trained for specific experimental conditions.
    • Technical Remarks: Implemented in PyTorch.

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    Uploaded by: Florian Pausch
    Created: 2025-06-29 21:55:14
    Updated: 2025-12-02 09:38:22