EC-SfM: Efficient Covisibility-Based Structure-From-Motion for Both Sequential and Unordered Images

Ye, Zhirong; Bao, Chong Gao; Zhou, Xin Zhu; Liu, H.; Bao, Hujun; Zhang, Guofeng

doi:10.1109/tcsvt.2023.3285479

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…The imag coordinates are converted into a 3D point cloud. Finally, a dense depth map is obtaine through a dense matching algorithm to ensure consistency in pixel brightness, adjacen pixel depth, and view visibility [104][105][106][107][108]. [101] to achieve robust, accurate, and scalable 3D structure reconstruction.…”

Section: Binocular Stereo Visionmentioning

confidence: 99%

A Comprehensive Review of Vision-Based 3D Reconstruction Methods

Zhou,

Wu,

Zuo

et al. 2024

Sensors

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With the rapid development of 3D reconstruction, especially the emergence of algorithms such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent years. 3D reconstruction technology provides crucial support for training extensive computer vision models and advancing the development of general artificial intelligence. With the development of deep learning and GPU technology, the demand for high-precision and high-efficiency 3D reconstruction information is increasing, especially in the fields of unmanned systems, human-computer interaction, virtual reality, and medicine. The rapid development of 3D reconstruction is becoming inevitable. This survey categorizes the various methods and technologies used in 3D reconstruction. It explores and classifies them based on three aspects: traditional static, dynamic, and machine learning. Furthermore, it compares and discusses these methods. At the end of the survey, which includes a detailed analysis of the trends and challenges in 3D reconstruction development, we aim to provide a comprehensive introduction for individuals who are currently engaged in or planning to conduct research on 3D reconstruction. Our goal is to help them gain a comprehensive understanding of the relevant knowledge related to 3D reconstruction.

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Section: Binocular Stereo Visionmentioning

confidence: 99%

A Comprehensive Review of Vision-Based 3D Reconstruction Methods

Zhou,

Wu,

Zuo

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Dual source geometric constraints based high precision online pose estimation

Zhang,

Chen,

Sun

et al. 2024

Engineering Applications of Artificial Intelligence

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Large-Scale 3D Reconstruction from Multi-View Imagery: A Comprehensive Review

Luo,

Zhang,

Liu

et al. 2024

Remote Sensing

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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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EC-SfM: Efficient Covisibility-Based Structure-From-Motion for Both Sequential and Unordered Images

Cited by 3 publications

References 59 publications

A Comprehensive Review of Vision-Based 3D Reconstruction Methods

A Comprehensive Review of Vision-Based 3D Reconstruction Methods

Dual source geometric constraints based high precision online pose estimation

Large-Scale 3D Reconstruction from Multi-View Imagery: A Comprehensive Review

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