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相关实验视频

Updated: May 10, 2025

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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.

Neural networks : the official journal of the International Neural Network Society
|April 24, 2025
PubMed
概括
此摘要是机器生成的。

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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.
关键词:
几何深度学习的几何深度学习在Gumbel-Softmax中使用.云点点点云点点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云点云选择令牌的选择变压器变压器变压器

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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.