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Depth Descent Synchronization in $${{\,\mathrm{\text {SO}}\,}}(D)

Maunu, Tyler ; Lerman, Gilad

International journal of computer vision, 2023-04, Vol.131 (4), p.968-986 [Periódico revisado por pares]

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Título:
Depth Descent Synchronization in $${{\,\mathrm{\text {SO}}\,}}(D)
Autor: Maunu, Tyler ; Lerman, Gilad
Assuntos: Algorithms
É parte de: International journal of computer vision, 2023-04, Vol.131 (4), p.968-986
Descrição: We give robust recovery results for synchronization on the rotation group, [Formula omitted]. In particular, we consider an adversarial corruption setting, where a limited percentage of the observations are arbitrarily corrupted. We develop a novel algorithm that exploits Tukey depth in the tangent space of [Formula omitted]. This algorithm, called Depth Descent Synchronization, exactly recovers the underlying rotations up to an outlier percentage of [Formula omitted], which corresponds to 1/4 for [Formula omitted] and 1/8 for [Formula omitted]. In the case of [Formula omitted], we demonstrate that a variant of this algorithm converges linearly to the ground truth rotations. We implement this algorithm for the case of [Formula omitted] and demonstrate that it performs competitively on baseline synthetic data.
Editor: Springer
Idioma: Inglês

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