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Autore principale: Wang, Yanwei
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2503.06818
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author Wang, Yanwei
author_facet Wang, Yanwei
contents 3D reconstruction of high-resolution target remains a challenge task due to the large memory required from the large input image size. Recently developed learning based algorithms provide promising reconstruction performance than traditional ones, however, they generally require more memory than the traditional algorithms and facing scalability issue. In this paper, we developed a generic approach, sub-image recapture (SIR), to split large image into smaller sub-images and process them individually. As a result of this framework, the existing 3D reconstruction algorithms can be implemented based on sub-image recapture with significantly reduced memory and substantially improved scalability
format Preprint
id arxiv_https___arxiv_org_abs_2503_06818
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Sub-Image Recapture for Multi-View 3D Reconstruction
Wang, Yanwei
Computer Vision and Pattern Recognition
3D reconstruction of high-resolution target remains a challenge task due to the large memory required from the large input image size. Recently developed learning based algorithms provide promising reconstruction performance than traditional ones, however, they generally require more memory than the traditional algorithms and facing scalability issue. In this paper, we developed a generic approach, sub-image recapture (SIR), to split large image into smaller sub-images and process them individually. As a result of this framework, the existing 3D reconstruction algorithms can be implemented based on sub-image recapture with significantly reduced memory and substantially improved scalability
title Sub-Image Recapture for Multi-View 3D Reconstruction
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2503.06818