HoloPointNet MICCAI 2025 Model Checkpoints, Data, and Reproducibility Artifacts

Abstract

This record contains data, trained model checkpoints, experiment logs, Hydra configuration files, and reproducibility artifacts associated with the MICCAI paper “HoloPointNet: A Deep Learning Framework for Efficient 3D Point Cloud Holography.” These files are intended to support the inference and reproducibility workflows provided in the accompanying GitHub repository: https://github.com/AnkitAmrutkar/HoloPointNet If you use these artifacts, please cite the associated MICCAI paper: Amrutkar, A. et al. (2026). HoloPointNet: A Deep Learning Framework for Efficient 3D Point Cloud Holography. In: Gee, J.C., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025. MICCAI 2025. Lecture Notes in Computer Science, vol 15970. Springer, Cham. https://doi.org/10.1007/978-3-032-05141-7_26

Authors 7

1. Ankit Amrutkar Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Ahmet Nazlioglu Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Björn Kampa Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Volkmar Schulz Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

   Hyperion Hybrid Imaging Systems GmbH

5. Johannes Stegmaier Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

6. Markus Rothermel

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Otto-von-Guericke-Universität Magdeburg

7. Dorit Merhof

   University of Regensburg

   Affiliation as printed

   University of Regensburg