Abstract

  Triangulation is a fundamental task in 3D computer vision. Unsurprisingly, it is a well-investigated problem with many mature algorithms. However, algorithms for robust triangulation, which are necessary to produce correct results in the presence of egregiously incorrect measurements(i.e., outliers), have received much less attention. The default approach to deal with outliers in triangulation is by random sampling. The randomized heuristic is not only suboptimal, it could, in fact, be computationally inefficient on large-scale datasets. In this paper, we propose a novel locally optimal algorithm for robust triangulation. A key feature of our method is to efficiently derive the local update step by plane sweeping a set of quasiconvex functions. Underpinning our method is a new theory behind quasiconvex plane sweep, which has not been examined previously in computational geometry. Relative to the random sampling heuristic, our algorithm not only guarantees deterministic convergence to a local minimum, it typically achieves higher quality solutions in similar runtimes.

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