MGM-AE:使用网状图形蒙蔽自编码器对3D形状进行自我监督学习
Zhangsihao Yang1, Kaize Ding2, Huan Liu1
1Arizona State University.
概括
本研究介绍了 Mesh Graph Masked Autoencoders (MGM-AE),用于对3D网格数据进行自我监督的学习. 新的图形掩盖方法显著提高了形状分类和细分任务的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 几何深度学习 几何深度学习
背景情况:
- 在3D网状数据上的自主监督学习面临着几何拓和任务设计的挑战.
- 现有的方法很难有效地建模不规则的网状结构.
研究的目的:
- 为3D网状数据提出一种新的自我监督学习方法.
- 为了利用网状面上的图形掩饰来有效地提取特征.
- 为了提高下游任务的性能,如形状分类和细分.
主要方法:
- 开发了使用面具自动编码的网状图面具自动编码器 (MGM-AE).
- 在由预训练面组成的网格图上应用图形掩盖.
- 在不同的掩盖比下训练和评估模型.
主要成果:
- 在形状分类 (90.8%的准确性在ModelNet40) 和细分 (78.5mIoU在ShapeNet) 上取得了最先进的结果.
- 与现有的网状编码器相比,证明了卓越的性能.
- 确定了最佳性能的最佳掩盖比率.
结论:
- MGM-AE有效用于大规模,未标记的3D网状数据集的预培训.
- 拟议的方法显示了在各种下游任务中提高性能的巨大潜力.
- 网状面上的图形掩盖是自我监督的3D网状学习的可行策略.
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