We propose a systematic approach for registering cross-source point clouds that come from different kinds of sensors. This task is especially challenging due to the presence of significant missing data, large variations in point density, scale difference, large proportion of noise, and outliers. The robustness of the method is attributed to the extraction of macro and micro structures. Macro structure is the overall structure that maintains similar geometric layout in cross-source point clouds. Micro structure is the element (e.g., local segment) being used to build the macro structure. We use graph to organize these structures and convert the registration into graph matching. With a novel proposed descriptor, we conduct the graph matching in a discriminative feature space. The graph matching problem is solved by an improved graph matching solution, which considers global geometrical constraints. Robust cross source registration results are obtained by incorporating graph matching outcome with RANSAC and ICP refinements. Compared with eight state-of-the-art registration algorithms, the proposed method invariably outperforms on Pisa Cathedral and other challenging cases. In order to compare quantitatively, we propose two challenging cross-source data sets and conduct comparative experiments on more than 27 cases, and the results show we obtain much better performance than other methods. The proposed method also shows high accuracy in same-source data sets.

We propose an efficient method to deal with the matching and registration problem found in cross-source point clouds captured by different types of sensors. This task is especially challenging due to the presence of density variation, scale difference, a large proportion of noise and outliers, missing data, and viewpoint variation. The proposed method has two stages: in the coarse matching stage, we use the ensemble of shape functions descriptor to select potential K regions from the candidate point clouds for the target. In the fine stage, we propose a scale embedded generative Gaussian mixture models registration method to refine the results from the coarse matching stage. Following the fine stage, both the best region and accurate camera pose relationships between the candidates and target are found. We conduct experiments in which we apply the method to two applications: one is 3D object detection and localization in street-view outdoor (LiDAR/VSFM) cross-source point clouds and the other is 3D scene matching and registration in indoor (KinectFusion/VSFM) cross-source point clouds. The experiment results show that the proposed method performs well when compared with the existing methods. It also shows that the proposed method is robust under various sensing techniques, such as LiDAR, Kinect, and RGB camera.

The existing state-of-the-art point descriptor relies on structure information only, which omits the texture information. However, texture information is crucial for our humans to distinguish a scene part. Moreover, the current learning-based point descriptors are all black boxes which are unclear how the original points contribute to the final descriptor. This paper proposes a new multimodal fusion method to generate a point cloud registration descriptor by considering structure and texture information. Specifically, a novel attention-fusion module is designed to extract the weighted texture information for descriptor extraction. In addition, we propose an interpretable module to explain our neural network by visually showing the original points contributing to the final descriptor. We use the descriptor's channel value as the loss to backpropagate to the target layer and consider the gradient as the significance of this point to the final descriptor. This paper moves one step further to explainable deep learning in the registration task. Comprehensive experiments on 3DMatch, 3DLoMatch and KITTI demonstrate that the multimodal fusion descriptor achieves state-of-the-art accuracy and improves the descriptor's distinctiveness. We also demonstrate our interpretable module in explaining the registration descriptor extraction.

Sampling noise and density variation widely exist in the point cloud acquisition process, leading to few accurate point-to-point correspondences. Since they rely on point-to-point correspondence search, existing state-of-the-art point cloud registration methods face difficulty in overcoming the sampling noise and density variation accurately or efficiently. Moreover, the recent state-of-the-art learning-based methods requires ground-truth transformation as supervised information which lead to large labor costs in real scenes. In this paper, our motivation is that two point-clouds are considered two samples from a unified Gaussian Mixture Model (UGMM). Then, we leverage the advantage of the statistic model to overcome the noise and density variants, and uses the alignment score in the UGMM to supervise the network training. To achieve this motivation, we propose a new unsupervised learning-based probabilistic registration algorithm to reconstruct the unified GMM and solve the registration problem simultaneously. The proposed method formulates the registration problem into a clustering problem, which estimates the posterior probability that classifies the points of two input point clouds to components of the unified GMM. A new feature interaction module is designed to learn the posterior probability using both the self and cross point cloud information. Then, two differential modules are proposed to calculate the GMM parameters and transformation matrices. Experimental results on synthetic and real-world point cloud datasets demonstrate that our unsupervised method achieves better registration accuracy and efficiency than the state-of-the-art supervised and semi-supervised methods in handling noisy and density variant point clouds.