基于深度学习的三维模型检索算法综述

Review of 3D Model Retrieval Algorithms Based on Deep Learning

近年来,深度学习被广泛应用于各个领域并取得了显著的进展,如何利用深度学习高效管理呈爆炸式增长的三维模型一直是一个研究热点。本文介绍了发展至今主流的基于深度学习的三维模型检索算法,并根据实验得出的算法性能评估分析了其优缺点。根据检索任务的不同,可将主要的三维模型检索算法分为两类:1)基于模型的三维模型检索方法,即检索对象与被检索对象都是三维模型,按照对三维模型的表示方式不同,可进一步分为基于体素、基于点云和基于视图的方法;2)基于二维图像的跨域三维模型检索方法,即检索对象是二维图像,被检索对象是三维模型,包括基于二维真实图像和基于二维草图的三维模型检索方法。最后,对基于深度学习的三维模型检索算法目前存在的问题进行分析和讨论,并展望未来发展的新方向。

In recent years,deep learning has been widely used and achieved significant development in various fields.How to utilize deep learning to effectively manage the explosive increasing 3D models becomes a hot topic.This paper introduces the mainstream algorithms for deep learning based 3D model retrieval and analyzes the advantages and disadvantages according to the experimental performance.In terms of the retrieval tasks,3D model retrieval algorithms are classified into two categories:(1)Modelbased 3D model retrieval algorithms require that both query and gallery are 3D models.It can be further divided into voxel-based method,point cloud-based method and view-based method in regard of different representations of 3D models.(2)For 2D image-based cross-domain 3D model retrieval algorithms,the query is 2D image while the gallery is 3D model.It can be classified to 2D real image-based method and 2D sketch-based method.Finally,we analyze and discuss existing issues of deep learning based 3D model retrieval methods,and predict possible promising directions for this research topic.