Point set registration with mixture framework and variational inference

Ma, Xinke; Xu, Shijin; Zhou, Jie; Yang, Qinglu; Yang, Yang; Yang, Kun; Ong, Sim Heng

doi:10.1016/j.patcog.2020.107345

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…Ma et al. [20] defined a Gaussian variational mixture model and used anisotropic components to weaken the effect of outliers in registration.…”

Section: Introductionmentioning

confidence: 99%

“…Mateo et al [18] and Zach et al [19] introduced the Bayesian framework into registration problems. Ma et al [20] defined a Gaussian variational mixture model and used anisotropic components to weaken the effect of outliers in registration.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In application of point clouds registration, the initial relative motions in the motion averaging problem are mostly estimated via the ICP algorithm. And some researchers ameliorated the cost function by adding weight of scan pairs or using different distances between corresponding points, such as state-of-the-art works in [3,9,12,13,17,20,26]. Jin et al [17] proposed the weighted LRS method of multiview registration and reduce the sparsity of the reconstructed matrix by using the anti-symmetry of the relative motion and overlap rate of scan pairs.…”

Section: Introductionmentioning

confidence: 99%

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Fast and robust motion averaging via angle constraints of multi‐view range scans

Zhang

et al. 2021

The Journal of Engineering

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The motion averaging problem arises when recovering the motions for a set of scans in three-dimensional (3D) reconstruction. Motion averaging is taken as an optimisation problem, whose solution can be obtained via matrix decomposition in principal component analysis. The latest motion averaging methods utilize the Cauchy kernel function or overlap rate to construct the weighted matrix in the cost function, yielding weighted lowrank sparse matrix decomposition solutions. Nevertheless, the over-reliance on the initial value and high computational complexity remain two major problems. Here, we aim to improve the motion averaging performance via a fast and robust method based on the angle constraints of multiple scans. The additional term representing the angle constraint is added into the original cost function. By doing this, two benefits are facilitated: 1) the iteration times for the optimisation process are reduced; and 2) it effectively mitigates the over-reliance on the initial value and thus caters for outliers and random noise of initial motions. Compared with state-of-the-art motion averaging methods, it is indicated that the proposed method is more efficient and robust for numerical simulations of using both simulated motions and real 3D scanning data, given a large range of angle constraint values.

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“…Ma et al. [20] defined a Gaussian variational mixture model and used anisotropic components to weaken the effect of outliers in registration.…”

Section: Introductionmentioning

confidence: 99%