数据驱动的现场表示和测量过程

Wanrong Hong, Sili Zhu, Jun Li
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引用次数: 0

摘要

用于表示空间分布物理属性的自然数学对象是三维场函数,它在应用科学和工程学(包括流体动力学和计算几何等领域)中非常普遍。这些对象的表示是以任务为导向的,通过各种适合特定领域的技术来实现。最近的一项突破涉及使用灵活的参数化表示,特别是通过神经网络,对一系列场函数进行建模。这种技术旨在为计算视觉任务揭示场,例如表示光散射场。这一技术的有效性带来了飞速的进步,使各种应用中的时间依赖性建模成为可能。本调查报告对可学习场表示领域的最新文献进行了翔实的分类,并对视觉计算应用领域进行了全面总结。此外,还讨论了场表示和学习方面的未决问题,这有助于阐明未来的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-Driven Field Representations and Measuring Processes
Natural mathematical objects for representing spatially distributed physical attributes are 3D field functions, which are prevalent in applied sciences and engineering, including areas such as fluid dynamics and computational geometry. The representations of these objects are task-oriented, which are achieved using various techniques that are suitable for specific areas. A recent breakthrough involves using flexible parameterized representations, particularly through neural networks, to model a range of field functions. This technique aims to uncover fields for computational vision tasks, such as representing light-scattering fields. Its effectiveness has led to rapid advancements, enabling the modeling of time dependence in various applications. This survey provides an informative taxonomy of the recent literature in the field of learnable field representation, as well as a comprehensive summary in the application field of visual computing. Open problems in field representation and learning are also discussed, which help shed light on future research.
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