Deep Learning for Digital Geometry Processing and Analysis: A Review
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摘要: 随着各种硬件传感器以及重建技术的快速发展，数字几何模型成为继音频、图像、视频之后的第4代数字媒体，并在多个领域得到广泛应用.传统的数字几何分析和处理方法主要建立在手工定义的模型特征之上，这类方法只对特定问题或者在特定条件下才有效.而深度学习，尤其是神经网络模型，在自然语言处理和图像处理方面的成功，展示了它作为数据特征提取工具的强大能力，因此越来越多地被用在数字几何处理领域.对近年来基于深度学习的数字几何处理与分析技术进行了综述，重点分析了模型匹配与检索、模型分类与分割、模型生成、模型修复与重建以及模型变形与编辑中的相关技术国内外最新研究进展，并指出了存在的主要问题和发展方向.Abstract: With the rapid development of various hardware sensors and reconstruction technologies, digital geometric models have become the fourth generation of digital multimedia after audio, image and video, and have been widely used in many fields. Traditional digital geometry processing and analysis are mainly based on manually defined features that can only be valid for specific problems or under specific conditions. The deep learning, especially the neural network model, in the success of natural language processing and image processing demonstrates its powerful ability as a feature extraction tool for data analysis, and is therefore gradually used in the field of digital geometry processing. In this paper, we review the works of digital geometry processing and analysis based on deep learning in recent years, carefully analyze the research progress of shape matching and retrieval, shape classification and segmentation, shape generation, shape completion and reconstruction and shape deformation and editing, and also point out some existing problems and a few possible directions of future works.
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