根据多视角图像进行大规模三维重建:全面回顾

Remote. Sens. Pub Date : 2024-02-22 DOI:10.3390/rs16050773
Haitao Luo, Jinming Zhang, Xiongfei Liu, Lili Zhang, Junyi Liu
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引用次数: 0

摘要

三维重建是在现实世界中表现虚拟现实的一项关键技术,在计算机视觉领域具有重要价值。大规模三维模型在智慧城市、导航、虚拟旅游、灾难预警和搜救任务等领域有着广阔的应用前景。遗憾的是,目前大多数基于图像的研究都优先考虑室内场景中三维重建的速度和准确性。虽然也有一些研究涉及大尺度场景,但一直缺乏系统性的综合研究来汇集大尺度场景三维重建领域的进展。因此,本文全面概述了一种利用大尺度场景多视角图像的三维重建技术。本文全面总结和分析了基于视觉的大尺度场景三维重建技术。三维重建算法广泛分为传统方法和基于学习的方法。此外,这些方法还可根据传感器是否主动用光源照射物体进行分类,从而分为主动方法和被动方法两类。本文简要介绍了两种主动方法,即结构光和激光扫描。然后,重点转向运动结构(SfM)、立体匹配和多视角立体(MVS),包括传统方法和基于学习的方法。此外,还介绍了基于神经辐射场的三维重建新方法。详细阐述了大规模场景中的工作流程和改进。随后,介绍了一些著名的数据集和各种三维重建任务的评估指标。最后,总结了三维重建技术在大规模室外场景应用中遇到的挑战,并预测了未来的发展趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-Scale 3D Reconstruction from Multi-View Imagery: A Comprehensive Review
Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development.
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