适应性令牌选择可扩展点云转换器的可适应性令牌选择
Alessandro Baiocchi1, Indro Spinelli2, Alessandro Nicolosi3
1Sapienza University of Rome, Department of Computer, Control and Management Engineering, Via Ariosto 25, Rome, 00185, Italy.
概括
适应点云转换器 (AdaPT) 通过动态选择令牌有效地处理大型3D点云. 这种几何深度学习模型可以降低计算成本,同时保持对现实应用的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 几何深度学习 几何深度学习
- 自然语言处理自然语言处理.
背景情况:
- 3D数据采集正在迅速增加,推动对高效点云处理模型的需求.
- 变压器在自然语言处理方面取得了成功,并且正在适应点云任务.
- 标准点云转换器 (PTs) 面临着由于点云大小的二次复杂性而导致的可扩展性挑战.
研究的目的:
- 开发一个高效的几何深度学习模型来处理大规模的3D点云.
- 为了解决现有点云变压器的计算可扩展性限制.
- 引入一个灵活的机制来管理推理过程中的计算成本.
主要方法:
- 提出了自适应点云变压器 (AdaPT),该变压器将自适应令牌选择机制集成到标准PT中.
- 在推断过程中实施动态令牌减少策略,以处理大型点云.
- 引入一个预算机制,以灵活调整计算成本而无需再培训.
主要成果:
- AdaPT显著降低了大型点云处理的计算复杂性.
- 与标准点云变压器相比,该模型保持了竞争力的准确性.
- 对点云分类任务的实验评估验证了AdaPT的效率和性能.
结论:
- 通过使用几何深度学习,AdaPT为点云处理提供了一种高效且可扩展的解决方案.
- 适应性代币选择和预算机制使灵活和计算效率高的推断成为可能.
- 在将变压器架构应用于大规模3D数据方面,AdaPT代表了重大进步.
相关概念视频
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