CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Batched Incremental Structure-from-Motion
Cui Hainan(崔海楠); Shuhan Shen(申抒含); Gao Xiang(高翔); Hu Zhanyi(胡占义)
2017
Conference NameInternational Conference on 3D Vision
Conference Date2017-10
Conference PlaceQingDao, China
Abstract
The incremental Structure-from-Motion (SfM) technique
has advanced in both robustness and accuracy, but the effi-
ciency and scalability remain its key challenges. In this pa-
per, we propose a novel batched incremental SfM technique
to tackle these problems in a unified framework, where two
iteration loops are contained. The inner loop is a tracks tri-
angulation loop, where a novel tracks selection method is
proposed to find a compact subset of tracks for the bundle
adjustment (BA). The outer loop is a camera registration
loop, where a batch of cameras are simultaneously added
to alleviate the drifting risk and reduce the running times
of BA. By the tracks selection and batched camera registra-
tion, we find these two iteration loops converge fast. Ex-
tensive experiments demonstrate that our new SfM system
performs similarly or better than many of the state-of-the-
art SfM systems in terms of camera calibration accuracy,
while is more efficient, robust and scalable for large-scale
scene reconstruction.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19771
Collection模式识别国家重点实验室_机器人视觉
Recommended Citation
GB/T 7714
Cui Hainan,Shuhan Shen,Gao Xiang,et al. Batched Incremental Structure-from-Motion[C],2017.
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