Narrow Results

Manual selection of features from massive unstructured point cloud data is a very time-consuming task that requires a considerable amount of human intervention. This work is motivated by the need of fast and simple algorithm to obtain robust, stable and well-localized interest points that are used for subsequent processing in computer vision real-time applications. This paper presents an algorithm for detection of interest points in three-dimensional (3D) point cloud data by using a combined 3D Sobel–Harris operator. The proposed algorithm is compared with six state-of-the-art approaches used to identify the true feature points. Extensive experiments were carried out using synthetic benchmark and real datasets. The datasets were selected with different sizes, features and scales. The results were evaluated against human generated ground truth and predefined feature points. Three measures were used to evaluate the algorithm accuracy, namely localization accuracy L_e, False Positive Error (FPE) and False Negative Errors (FNE). Also, the complexity analysis of the proposed algorithm is presented. The results show that the proposed algorithm can identify the interest points with accepted accuracy. It works directly on point cloud datasets and shows superiority when compared with other methods work on 3D mesh data.

Rock mass fraction is one of the main indexes to evaluate the blasting effect of mining. We take some rock blocks after blasting as the research objects and use 3D laser scanner to obtain the point cloud data of rock blocks. Then we use statistical filtering method to process the original point cloud data, and then calculate the point cloud data after pre-processing. We obtain the supervoxel clustering point cloud. On the supervoxel clustering algorithm, the concave convex criterion is used to fuse the clustering results. The regional growth algorithm is used to complete the segmentation of rock point cloud, so as to achieve the purpose of automatic recognition of blasting rock block contour. Based on the segmentation results of the rock block point cloud, the rock block point cloud with obvious characteristics is extracted, and the length of the long axis of the rock block is obtained according to the feature information of the rock block. The results show that the method can solve the defects of traditional measurement methods. The proposed recognition algorithm will meet the requirement of the intelligent of blasting fragmentation analysis. Additionally, it will satisfy the requirements of blasting quality analysis and evaluation.

The ground 3D laser scanning technology has the advantages such as normal traffic flow, large amount of data and high efficiency, so it is suitable for application in surveying and mapping of existing road reconstruction and expansion. This paper takes a specific road reconstruction and expansion project as an example to study the process which is suitable for 3D laser scanning technology from the survey program design, equipment selection, geodetic chain and target layout aspects. In the meantime, related software to complete the point cloud data filtering, splicing, coordinate conversion and simplification are utilized along with the CASS software to generate DTM model for road reconstruction and expansion design. It turns out that this technology has high data accuracy. According to the test results, the difference between the data obtained by this technology and by traditional measurement methods respectively is under 4 mm. It can fully meet the design requirements of road reconstruction and expansion and has very good application prospects.