对3D点云的标签高效深度学习的调查
概括
标签效率学习对于在点云处理中推进深度神经网络至关重要,克服了数据注释的高成本. 本次调查探讨了使用较少标记的点云数据有效训练模型的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 3D数据处理 3D数据处理
背景情况:
- 深度神经网络具有先进的点云学习.
- 收集大规模的,精确注释的点云是昂贵的,耗时的.
- 这种注释瓶限制了点云数据集的可扩展性和应用.
研究的目的:
- 为点云提供第一个关于标签效率学习的综合调查.
- 为了解决这个新兴领域的重要性,范围和进展.
- 根据数据先决条件和标签类型组织方法.
主要方法:
- 四个关键标签高效学习方法的分类:数据增强,域转移学习,弱监督学习和预训练的基础模型.
- 基于标签要求组织方法的分类学建议.
- 对现有方法,挑战和进展进行了广泛的文献审查.
主要成果:
- 确定数据增强,领域转移学习,弱监督学习和预训练的基础模型作为关键策略.
- 标签高效点云学习当前环境的结构化概述.
- 突出研究挑战和该领域的未来方向.
结论:
- 标签效率学习对于克服点云处理中的注释成本至关重要.
- 拟议的分类学提供了对各种方法的结构化理解.
- 需要进一步的研究来应对当前的挑战,并释放未来的潜力.
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With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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