全向视觉的表征学习、优化策略及应用综述

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hao Ai, Zidong Cao, Lin Wang
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引用次数: 0

摘要

全向图像(ODI)数据的拍摄范围为\(360^\circ \times 180^\circ \),比针孔相机宽得多,比传统的透视图像捕捉到更丰富的周围环境细节。近年来,客户级\(360^\circ \)相机的可用性使全方位视觉更受欢迎,深度学习(DL)的进步极大地激发了其研究和应用。本文对全向视觉深度学习的最新进展进行了系统、全面的综述和分析。它描述了与传统透视图像相反,将深度学习应用于全向图像时遇到的独特挑战和复杂性。我们的工作包括四个主要内容:(i)全面介绍全方位成像的原理和通常探索的ODI投影;系统地审查为对外直接投资量身定制的各种表示学习方法;深入研究全向视觉的优化策略;(iv)具有代表性的全方位视觉任务的深度学习方法的结构和层次分类,从视觉增强(例如,图像生成和超分辨率)到3D几何和运动估计(例如,深度和光流估计),以及对新兴研究方向的讨论;(v)概述前沿应用(例如自动驾驶和虚拟现实),并就当前的挑战和悬而未决的问题进行关键讨论,以引发更多的社区研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision

Omnidirectional image (ODI) data is captured with a field-of-view of \(360^\circ \times 180^\circ \), which is much wider than the pinhole cameras and captures richer surrounding environment details than the conventional perspective images. In recent years, the availability of customer-level \(360^\circ \) cameras has made omnidirectional vision more popular, and the advance of deep learning (DL) has significantly sparked its research and applications. This paper presents a systematic and comprehensive review and analysis of the recent progress of DL for omnidirectional vision. It delineates the distinct challenges and complexities encountered in applying DL to omnidirectional images as opposed to traditional perspective imagery. Our work covers four main contents: (i) A thorough introduction to the principles of omnidirectional imaging and commonly explored projections of ODI; (ii) A methodical review of varied representation learning approaches tailored for ODI; (iii) An in-depth investigation of optimization strategies specific to omnidirectional vision; (iv) A structural and hierarchical taxonomy of the DL methods for the representative omnidirectional vision tasks, from visual enhancement (e.g., image generation and super-resolution) to 3D geometry and motion estimation (e.g., depth and optical flow estimation), alongside the discussions on emergent research directions; (v) An overview of cutting-edge applications (e.g., autonomous driving and virtual reality), coupled with a critical discussion on prevailing challenges and open questions, to trigger more research in the community.

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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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