Recursive Structure from Motion
19 Dec 2016-pp 109-119
TL;DR: A technique that estimates the Structure from Motion (SFM) in a recursive fashion by recursively performing the SFM on the incoming set of images and updating the previously reconstructed structure with the structure estimated from the current set of photographs.
Abstract: In this paper we present a technique that estimates the Structure from Motion (SFM) in a recursive fashion. Traditionally successful SFM algorithms take the set of images and estimate the scene geometry and camera positions either using incremental algorithms or the global algorithms and do the refinement process [2] to reduce the reprojection error. In this work it is assumed that we don’t have complete image set at the start of the reconstruction process, unlike most of the traditional approaches present in the literature. It is assumed that the set of images come in at the regular intervals and we recursively perform the SFM on the incoming set of images and update the previously reconstructed structure with the structure estimated from the current set of images. The proposed system has been tested on two datasets which consist of 12 images and 60 images respectively and reconstructions obtained show the validity of our proposed technique.
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966 citations
TL;DR: A system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes that extends existing algorithms to meet the robustness and variability necessary to operate out of the lab and shows results on real video sequences comprising hundreds of thousands of frames.
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846 citations
17 Jun 2006
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480 citations
05 Sep 2010
TL;DR: The experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment.
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471 citations
06 Sep 2014
TL;DR: This work proposes a method for removing outliers from problem instances by solving simpler low-dimensional subproblems, which it refers to as 1DSfM problems.
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391 citations