.png){kind=icon}
返回
View-graph construction framework for robust and efficient structure-from-motion
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2020.107712.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A view-graph is vital for both the accuracy and robustness of structure-from-motion (SfM). Conventional matrix decomposition techniques treat all edges of view-graph equally; hence, many edge outliers are produced in matching pairs with fewer feature matches. To address this problem, we propose an incremental framework for view-graph construction, where the robustness of matched pairs that have a larger number of feature matches is propagated to their connected images. Given pairwise feature matches, a verified maximum spanning tree (VMST) is first constructed; for each edge in the VMST, we perform a local reconstruction and register its visible cameras. Based on the local reconstruction, pairwise relative geometries are computed and some new epipolar edges are produced. In this way, these newly computed edges inherit the robustness and accuracy of VMST, and by embedding them into VMST, our view-graph is constructed. We feed our view-graph into a standard SfM pipeline and compare this newly formed system with many of state-of-the-art SfM methods. The experimental results demonstrate that our view graph provides a better foundation for conventional SfM systems, and enables them to reconstruct both general and ambiguous images. ? 2020 Elsevier Ltd. All rights reserved.
Keyword:
Structure-from-motion
View-graph construction
Epipolar geometry computation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
分享收藏
The International Consensus on ANA Patterns (ICAP) in 2021—The 6th Workshop and Current Perspectives
OAAI
OAAI分享收藏 没有更多内容
没有更多内容