Abstract
Abstract. This paper presents a Structure from Motion approach for complex unorganized image sets. To achieve high accuracy and robustness, image triplets are employed and (an approximate) camera calibration is assumed to be known. The focus lies on a complete linking of images even in case of large image distortions, e.g., caused by wide baselines, as well as weak baselines. A method for embedding image descriptors into Hamming space is proposed for fast image similarity ranking. The later is employed to limit the number of pairs to be matched by a wide baseline method. An iterative graph-based approach is proposed formulating image linking as the search for a terminal Steiner minimum tree in a line graph. Finally, additional links are determined and employed to improve the accuracy of the pose estimation. By this means, loops in long image sequences are implicitly closed. The potential of the proposed approach is demonstrated by results for several complex image sets also in comparison with VisualSFM.
Highlights
Recent developments for Structure from Motion (SfM) techniques from unorganized image sets focus on large photo collections downloaded from the internet (Heinly et al, 2015; Snavely et al, 2008; Agarwal et al, 2009; Frahm et al, 2009; Havlena et al, 2010; Crandall et al, 2011)
Such collections can contain thousands or even millions of images comprising a very high redundancy and often moderate baselines. In contrast to these large photo collections, we focus on smaller image sets up to a few thousand images, but containing complex configurations comprising wide as well as weak baselines between images
This can be formulated as search for a terminal Steiner minimum tree (Lin and Xue, 2002): Given an undirected, weighted Graph G = (V, E) and a subset R ⊆ V of nodes, a Steiner tree is an acyclic subgraph of G that spans all terminals
Summary
Recent developments for Structure from Motion (SfM) techniques from unorganized image sets focus on large photo collections downloaded from the internet (Heinly et al, 2015; Snavely et al, 2008; Agarwal et al, 2009; Frahm et al, 2009; Havlena et al, 2010; Crandall et al, 2011) Such collections can contain thousands or even millions of images comprising a very high redundancy and often moderate baselines. The goal is a complete linking of all images in sets of moderate size to obtain accurate estimates of camera poses even for complex configurations consisting of wide as well as weak baselines.
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