  Ideal part editing should guarantee the diversity of edited parts, the
fidelity to the remaining parts, and the quality of the results. However,
previous methods do not disentangle each part completely, which means the
edited parts will affect the others, resulting in poor diversity and fidelity.
In addition, some methods lack constraints between parts, which need manual
selections of edited results to ensure quality. Therefore, we propose a
four-stage process for point cloud part editing: Segmentation, Generation,
Assembly, and Selection. Based on this process, we introduce SGAS, a model for
part editing that employs two strategies: feature disentanglement and
constraint. By independently fitting part-level feature distributions, we
realize the feature disentanglement. By explicitly modeling the transformation
from object-level distribution to part-level distributions, we realize the
feature constraint. Considerable experiments on different datasets demonstrate
the efficiency and effectiveness of SGAS on point cloud part editing. In
addition, SGAS can be pruned to realize unsupervised part-aware point cloud
generation and achieves state-of-the-art results.
