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.