Non-feature Points Research Articles

Point cloud simplification for the boundary preservation based on extracted four features

This paper proposes a simplification algorithm based on four feature parameters, aiming at solving the problem that the edge features cannot be retained due to the incompletely extracted sharp features during point cloud simplification. Firstly, K neighborhood searching is carried out for point cloud, and K neighborhood points are quickly found by a dynamic grid method. Then, four features including: the curvature of the point, the average of the normal angle of a point from a neighborhood point, the average distance between the point and the neighborhood point and the distance between the point and the center of gravity of the neighborhood point, are calculated according to the K neighborhood of the data point. The four parameters are used to define the feature discrimination parameters and feature thresholds, to compare the size and extract the feature points; finally, the non-feature points are reduced twice by the method of the bounding box, and the reduced point cloud and feature points are spliced to achieve the purpose of simplification. The experimental results show that the distance between the point and the center of gravity of the neighborhood has a great influence on the simplified model boundary, which effectively guarantees the accuracy of the simplified model.

Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship

After using a terrestrial laser scanner to acquire building facade point clouds, the extraction of feature lines can simplify the expression of building objects, thereby contributing to the accurate construction of building facade geometric models. To address the problems of missing extraction and low accuracy in existing methods, this study proposes a feature line extraction method for building facade point clouds by exploring the different spatial topological relationship between feature and non‐feature points. The method comprises three steps: feature point extraction, feature line generation, and feature line merging and optimization. First, feature points are extracted using the convex hull distance value and relative angle, as defined in this study. Thereafter, feature points are clustered by combining the random sample consensus and region growing algorithms, and the feature lines are obtained by utilizing the iterative weighted least squares method based on the IGGIII weight function. Finally, the feature lines are merged and optimized using an endpoint search method to improve the discontinuity and missing common endpoints of the original feature lines. The experimental results obtained using simulated and measured point cloud data show that this method can accurately extract feature points, and the extracted feature lines obtained from building facade point clouds have better accuracy and completeness, which is more practical than the existing methods and can be used in many applications such as building facade measurement and urban three‐dimensional modeling.

A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors.

Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm (MIWSA), is proposed in this paper. First, the point cloud is organized with a bounding box and kd-trees to find the neighborhood of each point, and the points are divided into small segments. Second, the feature index of each point is calculated to indicate the characteristics of the points. Third, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) are applied to weight these indexes to determine whether each point is a feature point. Fourth, non-feature points are judged as saved or abandoned according to their spatial relationship with the feature points. To verify the effect of the MIWSA, 3D model scanning datasets are calculated and analyzed, as well as field area scanning datasets. The accuracy for the 3D model scanning datasets is assessed by the surface area and patch numbers of the encapsulated surfaces, and that for field area scanning datasets is evaluated by the DEM error statistics. Compared with existing algorithms, the overall accuracy of the MIWSA is 5% to 15% better. Additionally, the running time is shorter than most. The experimental results illustrate that the MIWSA can simplify point clouds more precisely and uniformly.

Feature-preserving simplification framework for 3D point cloud.

To obtain a higher simplification rate while retaining geometric features, a simplification framework for the point cloud is proposed. Firstly, multi-angle images of the original point cloud are obtained with a virtual camera. Then, feature lines of each image are extracted by deep neural network. Furthermore, according to the proposed mapping relationship between the acquired 2D feature lines and original point cloud, feature points of the point cloud are extracted automatically. Finally, the simplified point cloud is obtained by fusing feature points and simplified non-feature points. The proposed simplification method is applied to four data sets and compared with the other six algorithms. The experimental results demonstrate that our proposed simplification method has the superiority in terms of both retaining geometric features and high simplification rate.

A new point cloud simplification method with feature and integrity preservation by partition strategy

The advancement of 3D scanning technology has gradually brought to light the issue of complex and time-consuming processing of high-density point cloud data. To address this need, this article proposes a method for point cloud simplification based on the concept of partitioning, which divides the point cloud's points into edge points, feature points, and non-feature points. On the basis of the normal angular difference, the edge points are extracted from the entire point cloud. The point cloud is then segmented by curvature into feature and non-feature regions using a region growing segmentation method. The feature points are determined by calculating the information entropy of the points in the feature region, whereas the non-feature points are extracted by voxel down-sampling the non-feature points. The experiment demonstrates that the proposed method effectively preserves the features and integrity of the point cloud while requiring less computational effort.

A New Simplification Algorithm for Scattered Point Clouds with Feature Preservation

High-precision and high-density three-dimensional point cloud models usually contain redundant data, which implies extra time and hardware costs in the subsequent data processing stage. To analyze and extract data more effectively, the point cloud must be simplified before data processing. Given that point cloud simplification must be sensitive to features to ensure that more valid information can be saved, in this paper, a new simplification algorithm for scattered point clouds with feature preservation, which can reduce the amount of data while retaining the features of data, is proposed. First, the Delaunay neighborhood of the point cloud is constructed, and then the edge points of the point cloud are extracted by the edge distribution characteristics of the point cloud. Second, the moving least-square method is used to obtain the normal vector of the point cloud and the valley ridge points of the model. Then, potential feature points are identified further and retained on the basis of the discrete gradient idea. Finally, non-feature points are extracted. Experimental results show that our method can be applied to models with different curvatures and effectively avoid the hole phenomenon in the simplification process. To further improve the robustness and anti-noise ability of the method, the neighborhood of the point cloud can be extended to multiple levels, and a balance between simplification speed and accuracy needs to be found.

Simplification method for 3D Terracotta Warrior fragments based on local structure and deep neural networks.

The emergence of the three-dimensional (3D) scanner has greatly benefited archeology, which can now store cultural heritage artifacts in computers and present them on the Internet. As many Terracotta Warriors have been predominantly found in fragments, the pre-processing of these fragments is very important. The raw point cloud of the fragments has lots of redundant points; it requires an excessively large storage space and much time for post-processing. Thus, an effective method for point cloud simplification is proposed for 3D Terracotta Warrior fragments. First, an algorithm for extracting feature points is proposed that is based on local structure. By constructing a k-dimension tree to establish the k-nearest neighborhood of the point cloud, and comparing the feature discriminant parameter and characteristic threshold, the feature points, as well as the non-feature points, are separated. Second, a deep neural network is constructed to simplify the non-feature points. Finally, the feature points and the simplified non-feature points are merged to form the complete simplified point cloud. Experiments with the public point cloud data and the real-world Terracotta Warrior fragments data are designed and conducted. Excellent simplification results were obtained, indicating that the geometric feature can be preserved very well.

Point Cloud Simplification Algorithm for Oral Cavity Model based on Feature Significance Evaluation

Point cloud simplification is an important pretreatment method for the surface reconstruction of oral cavity model with complex surface features and point cloud uneven sampling. The difficulty lies in how to accurately extract feature points and efficiently simplify redundant points to meet the requirements of high-precision reconstruction of oral cavity model. In this paper, we proposed a point cloud simplification algorithm for oral cavity model based on feature significance evaluation. First, point cloud down sampling is used to simplify the original points and k-neighboring points are searched by dynamic search cube method. Second, the feature significance evaluation formula is constructed by using feature measurement factors such as curvature, average projection distance and average spatial distance to extract the feature points. Finally, a hybrid segmentation method is used to segment remaining points, and the simplification rules and octree structure are used to realize the non-uniform simplification of the non-feature points. The experimental results show that the proposed algorithm can well retain the detail features of complex surfaces such as tooth contour and tooth structure, avoid the occurrence of holes in flat areas and the expansion of interdental holes. The algorithm has achieved good effects in terms of simplicity and accuracy.

Deep feature-preserving normal estimation for point cloud filtering

Point cloud filtering, the main bottleneck of which is removing noise (outliers) while preserving geometric features, is a fundamental problem in 3D field. The two-step schemes involving normal estimation and position update have been shown to produce promising results. Nevertheless, the current normal estimation methods including optimization ones and deep learning ones, often either have limited automation or cannot preserve sharp features. In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features. It is a learning method and thus achieves automatic prediction for normals. For training phase, we first generate patch based samples which are then fed to a classification network to classify feature and non-feature points. We finally train the samples of feature and non-feature points separately, to achieve decent results. Regarding testing, given a noisy point cloud, its normals can be automatically estimated. For further point cloud filtering, we iterate the above normal estimation and a current position update algorithm for a few times. Various experiments demonstrate that our method outperforms state-of-the-art normal estimation methods and point cloud filtering techniques, in terms of both quality and quantity.

Modeling and Prediction Method for CNC Machine Tools’ Errors Based on Spatial Feature Points

In order to improve the precision of CNC machine tools effectively, a method for modeling and predicting their spatial errors based on spatial feature points was proposed. Taking three‐axis vertical CNC machine tools as the research object, we think that the whole space formed by machine tools’ working can be seen as the combination of a number of cubes, whose vertices are considered to be feature points, and others in the cubes are called nonfeature points. So, each nonfeature point’s errors can be predicted by the cube’s eight vertices’ errors. Based on the above ideas, an approach including the installing instrument for measuring any spatial feature point’s errors was put forward. In this way, all data of the feature points’ errors could be obtained. Moreover, according to these error data, the prediction model of nonfeature points’ errors was established by using the internal division ratio method. The method has the advantages of small interpolation operation, easy integration in the numerical control system, and high compensation precision. Finally, an example was used to prove its effectiveness and feasibility.

Fast registration of UAV aerial images based on improved optical-flow model combined with feature-point matching

With a large number of registration algorithms proposed, image registration techniques have achieved rapid development. However, there still exist many deficiencies in aerial images registration where high speed and accuracy are difficult to simultaneously achieve for real-time processing. In order to achieve large-scale and high-precision image registration for unmanned aerial vehicle(UAV) aerial images, a novel and fast sub-pixel image registration algorithm based on improved optical-flow model combined with feature-point matching is proposed in this paper. Firstly, the coarse selection at the feature level is achieved by using the feature-point model, which reduces the number of non-feature points so as to speed up the coarse registration process. Then, the improved pyramid optical-flow model is adopted in the neighborhood of the coarse point, and the sub-pixel fast location is achieved by the bidirectional search strategy. Simulation experiment results show that compared with common image registration based LK optical-flow or feature-point matching, our proposed algorithm will greatly reduce space complexity and time complexity without losing accuracy.

Multi-Normal Estimation via Pair Consistency Voting.

The normals of feature points, i.e., the intersection points of multiple smooth surfaces, are ambiguous and undefined. This paper presents a unified definition for point cloud normals of feature and non-feature points, which allows feature points to possess multiple normals. This definition facilitates several succeeding operations, such as feature points extraction and point cloud filtering. We also develop a feature preserving normal estimation method which outputs multiple normals per feature point. The core of the method is a pair consistency voting scheme. All neighbor point pairs vote for the local tangent plane. Each vote takes the fitting residuals of the pair of points and their preliminary normal consistency into consideration. Thus the pairs from the same subspace and relatively far off features dominate the voting. An adaptive strategy is designed to overcome sampling anisotropy. In addition, we introduce an error measure compatible with traditional normal estimators, and present the first benchmark for normal estimation, composed of 152 synthesized data with various features and sampling densities, and 288 real scans with different noise levels. Comprehensive and quantitative experiments show that our method generates faithful feature preserving normals and outperforms previous cutting edge normal estimation methods, including the latest deep learning based method.

Parametric Reshaping of Portrait Images for Weight-change.

We present an easy-to-use parametric image retouching method for thinning or fattening a face in a single portrait image while maintaining a close similarity to the source image. First, our method reconstructs a 3D face from the input face image using a morphable model. Second, according to the linear regression equation derived from the depth statistics of the soft tissue in the face and the user-set parameters of weight-change degree, we calculate the new positions of the feature points. The Laplacian deformation method is then used for non-feature points in the 3D face model. Our model-based reshaping process can achieve globally consistent editing effects without noticeable artifacts. We seamlessly blend the reshaped face region with the background using image retargeting method based on mesh parametrization. The effectiveness of our algorithm is demonstrated by experiments and user study.

Multipurpose watermarking for vector map protection and authentication

The rapid flourishing of GIS applications has made the copyright protection and content authentication of vector maps two important issues in the digital world. In this paper, a multipurpose watermarking scheme is proposed for the scenarios when the two requirements are needed simultaneously. In the scheme, robust watermark and fragile watermarks are embedded into the host map simultaneously. To avoid the interference between the two kinds of watermarks, robust watermark is embedded into the feature points of the objects in the map, while fragile watermarks are embedded into the nonfeature points. Due to the independence of the feature points and non-feature points, the robust watermark and fragile watermarks can be detected independently. After watermark detection, the robust watermark can be used for copyright protection, and the fragile watermarks can be used for tamper localization and characterization. The fragility of the proposed scheme is analyzed theoretically, and both the robustness and the fragility are verified by a set comprehensive experiments.

An Efficient Planar Feature Fitting Method Using Point Cloud Simplification and Threshold-Independent BaySAC

Three-dimensional laser scanning can acquire point cloud data with high spatial resolution. However, for practical applications, such as point cloud fitting and 3-D reconstruction, there is usually significant data redundancy, which reduces the operational efficiency. In this letter, we propose a fast point cloud fitting algorithm that uses point cloud simplification to preserve feature boundaries and threshold-independent Bayesian sampling consensus (BaySAC) to fit planar features. We first extract the point features, such as corner points and contour points, using a smoothing analysis of the vicinities of scattered points and an angle analysis of vectors based on search points and their adjacent points. Then, keeping all the feature points, we thin the nonfeature points by constructing a cube grid. Finally, based on the least median squares and the BaySAC algorithm, we propose a robust nonthreshold-dependent method to perform the rapid fitting of planar features in the point cloud after thinning. We used three sets of point cloud data acquired using a 3-D laser scanner to verify the accuracy and efficiency of the planar feature fitting method. The experimental results indicate that the method can extract finer planar features and has significantly better accuracy and computational efficiency than the classical random sample consensus algorithm for the fitting of planar features without using a threshold.

Progressive Contours Simplification Approach Constrained by Terrain Feature

To maintain terrain details during contour group simplification, a progressive simplification approach is proposed. First, terrain feature points and lines are extracted from contours. Second, both the extracted terrain feature points and lines are taken as constraints and quantified as control variables. At last with the idea of progressive graphics simplification, the non-feature points, feature points, feature lines and their associated curves on contours are selected or deleted progressively to realize dynamic simplification of contours. Experimental results show that this approach can effectively improve the intellectualized degree of contours simplification.

Distance Measurement Algorithm Based on Binocular Stereo Vision

Distance measurement technology of binocular stereo vision has the advantages of wide-range detection, simplicity and reliability. The method is widely applied to robot obstacle avoidance and path planning. Binocular stereo vision can only measure the distance of images feature points generally. However, more information about distance of non-feature points is also needed to acquire in practical applications. This paper proposes a stereo distance measurement method, which can measure distance of points whether it is a feature point or not based on a dense matching method. A dense parallax map is obtained by the graph-cuts algorithm. On the basis of the calibration parameters of binocular camera and the left and right image dense parallax map, the three-dimensional coordinates of the any points and their distance will be gotten. The true image experiment has proved the feasibility of this algorithm with high accuracy and maneuverability.

Nonlinear scrambling-based reversible watermarking for 2D-vector maps

The reversible watermarking technique is suitable for vector maps due to its reversibility after watermark extraction. In this paper, a novel reversible watermarking scheme based on the idea of nonlinear scrambling is proposed. It begins with feature point extraction. To avoid the high-precision vector data being illegally used by unauthorized users, the algorithm nonlinearly scrambles the relative position of feature points. Then based on the proposed reversible embedding, both scrambled feature points and nonfeature points are taken as cover data, the coordinates of which are modified to embed both watermark data and feature point identification data. Finally, combined with the scrambling secret key, the original vector data can be exactly recovered with watermark extraction. Comprehensive experimental results validate that the scheme could effectively prevent the high-precision vector data from being illegally used with maintaining the basic shape of each polyline, simultaneously.

New robust watermarking algorithm for vector data

Because the existing vector data watermarking algorithm is insufficiently robust for compression and cropping, and has no sufficient control on map deformation, a new watermarking algorithm was proposed in this paper. Its main features are: ➀ it selected feature points and non-feature points from the sequence of Douglas, and then embedded watermarking points by wavelet transform, which is based on layer segmentation; ➁ it enhanced the map deformation control design. Furthermore, it increased the map graphic deformation control design. The algorithm was applied to experimental data, and the test results showed that the algorithm had good robustness on graphics geometric transformation, the addition, deletion and cropping of points in graph layers. Also, the graph distortion had been well controlled.

Regions Segmentation Algorithm of Triangle Meshes Based on Normal Vector

An approach of regions segmentation algorithm in triangle meshes based on threshold angle of the normal vectors is proposed. In this paper, a set of threshold angles is used to identify features and segment different surfaces, and then the non-feature regions based on least squares method are fitted and the corresponding fitting precision are obtained. Finally, the best threshold angle of each region is determined according to the comparison the above fitting precisions and each non-feature region points are obtained exactly. This method can improve the precision of feature extraction and region segmentation in some hybrid blend region. Examples are given to prove the effectiveness and the processing flexibility and stability of the proposed approach.