In this paper, we present a complete pipeline for computing structure-from-motion from the sequences of spherical images. We revisit problems from multiview geometry in the context of spherical images. In particular, we propose methods suited to spherical camera geometry for the spherical-n-point problem (estimating camera pose for a spherical image) and calibrated spherical reconstruction (estimating the position of a 3-D point from multiple spherical images). We introduce a new probabilistic interpretation of spherical structure-from-motion which uses the von Mises-Fisher distribution to model noise in spherical feature point positions. This model provides an alternate objective function that we use in bundle adjustment. We evaluate our methods quantitatively and qualitatively on both synthetic and real world data and show that our methods developed for spherical images outperform straightforward adaptations of methods developed for perspective images. As an application of our method, we use the structure-from-motion output to stabilise the viewing direction in fully spherical video.

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noise in spherical feature point computing structurefrommotion spherical image and calibrated spherical reconstruction viewing direction problems from multiview geometry sphericalnpoint problem estimating camera pose von misesfisher distribution probabilistic interpretation of spherical structurefrommotion synthetic and real world data perspective objective function pipeline 3d point

Hao Guan,.Structure-From-Motion in Spherical Video Using the von Mises-Fisher Distribution. 26 (2),711-723.

Hao Guan,"Structure-From-Motion in Spherical Video Using the von Mises-Fisher Distribution" 26

Hao Guan,"Structure-From-Motion in Spherical Video Using the von Mises-Fisher Distribution"