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HSfM: Hybrid Structure-from-Motion
Cui Hainan(崔海楠); Xiang Gao(高翔); Shen Shuhan(申抒含); Hu Zhanyi(胡占义)
Conference NameConference on Computer Vision and Pattern Recognition
Conference Date2017-06
Conference PlaceUSA,Honolulu
Structure-from-Motion (SfM) methods can be broadly
categorized as incremental or global according to their
ways to estimate initial camera poses. While incremental
system has advanced in robustness and accuracy, the ef-
ficiency remains its key challenge. To solve this problem,
global reconstruction system simultaneously estimates al-
l camera poses from the epipolar geometry graph, but it
is usually sensitive to outliers. In this work, we propose
a new hybrid SfM method to tackle the issues of efficien-
cy, accuracy and robustness in a unified framework. More
specifically, we propose an adaptive community-based ro-
tation averaging method first to estimate camera rotations
in a global manner. Then, based on these estimated camera
rotations, camera centers are computed in an incremental
way. Extensive experiments show that our hybrid method
performs similarly or better than many of the state-of-the-
art global SfM approaches, in terms of computational effi-
ciency, while achieves similar reconstruction accuracy and
robustness with two other state-of-the-art incremental SfM
Document Type会议论文
Recommended Citation
GB/T 7714
Cui Hainan,Xiang Gao,Shen Shuhan,et al. HSfM: Hybrid Structure-from-Motion[C],2017.
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