Point cloud registration, as the first step for the use of point cloud data, has attracted increasing attention. In order to obtain the entire point cloud of a scene, the registration of point clouds from multiple views is necessary. In this paper, we propose an automatic method for the coarse registration of point clouds. The 2D lines are first extracted from the two point clouds being matched. Then, the line correspondences are established and the 2D transformation is calculated. Finally, a method is developed to calculate the displacement along the z-axis. With the 2D transformation and displacement, the 3D transformation can be easily achieved. Thus, the two point clouds are aligned together. The experimental results well demonstrate that our method can obtain high-precision registration results and is computationally very efficient. In the experimental results obtained by our method, the biggest rotation error is 0.5219^o, and the biggest horizontal and vertical errors are 0.2319 m and 0.0119 m, respectively. The largest total computation time is only 713.4647 s.

As one of the most common features, 3D line segments provide visual information in scene surfaces and play an important role in many applications. However, due to the huge, unstructured, and non-uniform characteristics of building point clouds, 3D line segment extraction is a complicated task. This paper presents a novel method for extraction of 3D line segment features from an unorganized building point cloud. Given the input point cloud, three steps were performed to extract 3D line segment features. Firstly, we performed data pre-processing, including subsampling, filtering and projection. Secondly, a projection-based method was proposed to divide the input point cloud into vertical and horizontal planes. Finally, for each 3D plane, all points belonging to it were projected onto the fitting plane, and the α-shape algorithm was exploited to extract the boundary points of each plane. The 3D line segment structures were extracted from the boundary points, followed by a 3D line segment merging procedure. Corresponding experiments demonstrate that the proposed method works well in both high-quality TLS and low-quality RGB-D point clouds. Moreover, the robustness in the presence of a high degree of noise is also demonstrated. A comparison with state-of-the-art techniques demonstrates that our method is considerably faster and scales significantly better than previous ones. To further verify the effectiveness of the line segments extracted by the proposed method, we also present a line-based registration framework, which employs the extracted 2D-projected line segments for coarse registration of building point clouds.

In the last decades, 3-D object recognition has received significant attention. Particularly, in the presence of clutter and occlusion, 3-D object recognition is a challenging task. In this article, we present an object recognition pipeline to identify the objects from cluttered scenes. A highly descriptive, robust, and computationally efficient local shape descriptor (LSD) is first designed to establish the correspondences between a model point cloud and a scene point cloud. Then, a clustering method, which utilizes the local reference frames (LRFs) of the keypoints, is proposed to select the correct correspondences. Finally, an index is developed to verify the transformation hypotheses. The experiments are conducted to validate the proposed object recognition method. The experimental results demonstrate that the proposed LSD holds high descriptor matching performance and the clustering method can well group the correct correspondences. The index is also very effective to filter the false transformation hypotheses. All these enhance the recognition performance of our method.

Registration of point clouds is vital in point cloud data processing. By registering, the point cloud data from different views are transformed into a common coordinate system. In the iterative closest projected point (ICPP) method, the three nearest points are used to form a patch or plane and the performance is greatly affected by noise. More points may be used to construct a plane to reduce the effect of noise, but such a technique may not be suited for the scenarios where the physical surface is not a 2-D pane but a curved surface. In this paper, the iterative closest optimal plane (ICOPlane) method is developed. We propose a method of searching the optimal plane over a possible curved surface for registration of point clouds. In addition, in order to consider the errors of all variables, a constrained weighted total least squares algorithm is derived to estimate the plane parameters and transformation parameters. Both simulated and real experiments are carried out to examine the performance of the developed method, and experimental results demonstrate that the developed method can produce more accurate transformation parameters in comparison with the ICPP method.

This paper presents a novel method for segmentation of planar feature from unorganized point cloud based on 2D Hough Transform and octree. Given the input point cloud, three steps are performed to segment planar features. Firstly, the original point cloud is sampled and projected to the X-Y plane, and an extended 2D Hough transform algorithm is employed to extract the line segments. The selecting weight iteration method is used to calculate line equations and endpoint coordinates of those line segments. The space geometric equations of the vertical planes are then determined. Secondly, the octree structure of the original point cloud is established, and then the exact endpoint coordinates of the line segment are used to design a cube perpendicular to the X-Y plane and all points held by the cube are extracted. The distance from each of the extracted points inside the cube to its corresponding facade is calculated, if the distance is less than the predefined threshold, the point is regarded as a point inside the facade. Finally, all the facade points are removed from the original point cloud, and remaining point cloud is sampled and projected to the X-Z plane. The above process is repeated to extract horizontal planes. Evaluation experiments are performed by analyzing the performance of our method in four different scenes. The experimental results indicate that the proposed algorithm is suitable for segmentation of building planar features in different scenes. A comparison with competing techniques shows that our approach is considerably faster and scales significantly better than previous ones.

Registration is usually the first step for the usage of point cloud data. Registration of point clouds is crucial in numerous practical applications. In order to quickly and accurately align point clouds, we combine the local shape descriptor and generalized four-point congruent set (G4PCS) algorithm. A descriptor with high computation efficiency is chosen to establish the correspondences. Then, the constraints of the G4PCS algorithm are applied and three angular constraints are proposed. These constraints are divided into three groups according to their computation complexity. The three groups of constraints are used to accelerate the searching process of four correct correspondences. The experiments are performed to compare our method with several existing methods. The experimental results show that our registration method has the best registration precision and computation efficiency. The introduction of the local shape descriptor can enhance both registration precision and computation efficiency. The proposed three angular constraints can also further boost the search process.