Point Cloud Part Editing: Segmentation, Generation, Assembly, and Selection
This work addresses part editing in point clouds for applications like 3D modeling and computer vision, offering a novel method to overcome limitations in diversity and fidelity, though it is incremental in building on existing part editing techniques.
The paper tackles the problem of point cloud part editing by proposing a four-stage process (Segmentation, Generation, Assembly, Selection) and the SGAS model, which uses feature disentanglement and constraint strategies to improve diversity, fidelity, and quality, achieving state-of-the-art results in unsupervised part-aware point cloud generation.
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.