Krys4ta1/Generative-Adversarial-Networks-for-High-Fidelity-3D-Point-Cloud-Completion: Generative Adversarial Networks for High-Fidelity 3D Point Cloud Completion

This release contains the source code for our submitted manuscript: " Di Zhao, Sizhe Mao, et al., Generative Adversarial Networks for High-Fidelity 3D Point Cloud Completion, submitted to Scientific Reports, 2026."

Included in this release:

• Python scripts for the GAN model and training
• Sample completion of chair point clouds
• README with installation instructions and usage examples

Please cite the manuscript when using this code or dataset.