Tool: Mesh2PPM 1.0.0

Tool: Mesh2PPM 1.0.0 (2022-2024)

Subtitle: Weights of DNN instances estimating local BezierPPM parameters from pinna images

Cite as: Pausch, F., Perfler, 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

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Metadata

Creators:

Pausch, Florian , Acoustics Research Institute
Perfler, Felix , Acoustics Research Institute
Holighaus, Nicki , Acoustics Research Institute
Majdak, Piotr , Acoustics Research Institute

Publishers:

The SONICOM Ecosystem of the Austrian Academy of Sciences

Rightsholders:

Acoustics Research Institute

Keywords:

physically based modeling
computer vision
neural network
Vision Transformer
supervised learning
regression

Relations:

Mesh2PPM 1.0.0 requires the Tool BezierPPM 3.0 (2024).
Mesh2PPM 1.0.0 requires the Tool PyBezierPPM 3.0 (2024).
Mesh2PPM 1.0.0 is derived from the Database Mesh2PPM 1.0.0: Training (2022-2024).

Other:

DOI: not assigned yet
Uploaded by: Florian Pausch
Date (created): 2025-06-29 21:55:14 (GMT)
Date (updated): 2025-07-15 23:03:57 (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
Methods: Python, PyTorch

Comments

1 comments found:

Florian Pausch (2025-06-30 13:07:05): The Python code for the training of Mesh2PPM and the weights of the trained DNN instances will be published upon manuscript publication.

Uploaded by: Florian Pausch
Created: 2025-06-29 21:55:14
Updated: 2025-07-15 23:03:57