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

arXiv: https://arxiv.org/abs/2103.11084 · HTML: https://mlsanity.com/paper/2103.11084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 61 references that are in the map.
- First posted on arXiv 20 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://mlsanity.com/paper/2011.13005/) (2020)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration](https://mlsanity.com/paper/1605.03344/) (2016)
- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [3DRegNet: A Deep Neural Network for 3D Point Registration](https://mlsanity.com/paper/1904.01701/) (2019)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Learning multiview 3D point cloud registration](https://mlsanity.com/paper/2001.05119/) (2020)
- [FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization](https://mlsanity.com/paper/1811.10136/) (2018)
- [Rotational Projection Statistics for 3D Local Surface Description and Object Recognition](https://mlsanity.com/paper/1304.3192/) (2013)
- [Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization](https://mlsanity.com/paper/1609.01466/) (2016)
- [Registration of multi-view point sets under the perspective of expectation-maximization](https://mlsanity.com/paper/2002.07464/) (2020)
- [Simultaneous merging multiple grid maps using the robust motion averaging](https://mlsanity.com/paper/1706.04463/) (2017)
- [Robust Motion Averaging under Maximum Correntropy Criterion](https://mlsanity.com/paper/2004.09829/) (2020)
- [Weighted Motion Averaging for the Registration of Multi-View Range Scans](https://mlsanity.com/paper/1702.06264/) (2017)
- [K-means clustering for efficient and robust registration of multi-view point sets](https://mlsanity.com/paper/1710.05193/) (2017)

## Built on by (most cited first, 1 of 1)

- [3D Registration in 30 Years: A Survey](https://mlsanity.com/paper/2412.13735/) (2024)

## Abstract (arXiv)

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.