A Post-Scan Point Cloud Colorization Method for Cultural Heritage Documentation

The 3D laser scanning technique is important for cultural heritage documentation. The laser itself normally does not carry any color information, so it usually requires an embedded camera system to colorize the point cloud. However, when the embedded camera system fails to perform properly under some external interferences, a post-scan colorization method is always desired to improve the point cloud visuality. This paper presents a simple but efficient point cloud colorization method based on a point-to-pixel orthogonal projection under an assumption that the orthogonal and perspective projections can produce similar effects for a planar feature as long as the target-to-camera distance is relatively short (within several meters). This assumption was verified by a simulation experiment, and the results show that only approximately 5% of colorization error was found at a target-to-camera distance of 3 m. The method was further verified with two real datasets collected for the cultural heritage documentation. The results showed that the visuality of the point clouds for two giant historical buildings had been greatly improved after applying the proposed method.

A Closed-Form Solution to Planar Feature-Based Registration of LiDAR Point Clouds

Since pairwise registration is a necessary step for the seamless fusion of point clouds from neighboring stations, a closed-form solution to planar feature-based registration of LiDAR (Light Detection and Ranging) point clouds is proposed in this paper. Based on the Plücker coordinate-based representation of linear features in three-dimensional space, a quad tuple-based representation of planar features is introduced, which makes it possible to directly determine the difference between any two planar features. Dual quaternions are employed to represent spatial transformation and operations between dual quaternions and the quad tuple-based representation of planar features are given, with which an error norm is constructed. Based on L2-norm-minimization, detailed derivations of the proposed solution are explained step by step. Two experiments were designed in which simulated data and real data were both used to verify the correctness and the feasibility of the proposed solution. With the simulated data, the calculated registration results were consistent with the pre-established parameters, which verifies the correctness of the presented solution. With the real data, the calculated registration results were consistent with the results calculated by iterative methods. Conclusions can be drawn from the two experiments: (1) The proposed solution does not require any initial estimates of the unknown parameters in advance, which assures the stability and robustness of the solution; (2) Using dual quaternions to represent spatial transformation greatly reduces the additional constraints in the estimation process.

A Novel Indoor Structure Extraction Based on Dense Point Cloud

Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures.

An Establishing Method of the Datum Feature Simulator Based on CPVM Model

A general approach to automatically calculate a datum feature simulator (DFS) for a real part model is proposed in this paper. The geometric errors of the real part are represented by the controlling point variation model (CPVM) of the geometric feature, and the geometric deviations are simulated and generated by the Monte Carlo method. The linear feature and planar feature CPVM models are first introduced; these models can simulate all possible size, position, form, and orientation variations. Furthermore, these models are compatible with the ASME/ISO Standards for geometric tolerances. The determining rules of DFS based on the CPVM model are presented, according to the definitions of DFS by ASME standards. The CPVM models for three common datum features, i.e., the planar datum feature, cylindrical datum feature, and prismatic datum feature, are then established, and the algorithms to determine the corresponding DFSs for a different order of datum precedence are developed. An example is given to demonstrate the establishing method.