
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.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
