3DMNDT:3D multi-view registration method based on the normal distributions transform

• URL: http://arxiv.org/abs/2103.11084v1
• Date: Sat, 20 Mar 2021 03:20:31 GMT
• Title: 3DMNDT:3D multi-view registration method based on the normal distributions transform
• Authors: Jihua Zhu and Di Wang and Jiaxi Mu and Huimin Lu and Zhiqiang Tian and Zhongyu Li
• Abstract summary: This paper proposes a novel multi-view registration method, named 3D multi-view registration based on the normal distributions transform (3DMNDT) The proposed method integrates the K-means clustering and Lie algebra solver to achieve multi-view registration. Experimental results tested on benchmark data sets illustrate that the proposed method can achieve state-of-the-art performance for multi-view registration.
• Score: 23.427473819499145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The normal distributions transform (NDT) is an effective paradigm for the point set registration. This method is originally designed for pair-wise registration and it will suffer from great challenges when applied to multi-view registration. Under the NDT framework, this paper proposes a novel multi-view registration method, named 3D multi-view registration based on the normal distributions transform (3DMNDT), which integrates the K-means clustering and Lie algebra solver to achieve multi-view registration. More specifically, the multi-view registration is cast into the problem of maximum likelihood estimation. Then, the K-means algorithm is utilized to divide all data points into different clusters, where a normal distribution is computed to locally models the probability of measuring a data point in each cluster. Subsequently, the registration problem is formulated by the NDT-based likelihood function. To maximize this likelihood function, the Lie algebra solver is developed to sequentially optimize each rigid transformation. The proposed method alternately implements data point clustering, NDT computing, and likelihood maximization until desired registration results are obtained. Experimental results tested on benchmark data sets illustrate that the proposed method can achieve state-of-the-art performance for multi-view registration.

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