Surface Reconstruction from Point Cloud for Vietnamese Historical Printing Woodblocks: DeepFit model in Estimating Normal Vectors
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Normal
Surface reconstruction
Vietnamese
Solid modeling
Surface reconstruction is an important part of CAD technology modeling in reverse engineering,surface reconstruction of scattered point cloud technology based on three-dimensional is a research focus.To the application of surface reconstruction of three-dimensional scan data points in the actual system,the article take this three-dimensional scattered point cloud as the research object,proposed a non-uniform rational B-spline surface construction method,which targets according to the known data point approximation surface.Through the system application,this method is an effective method of surface fitting reconstruction.
Reverse engineering
Surface reconstruction
B-spline
3D Reconstruction
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Reconstructing the surface of 3D point clouds is a reconstruction from a cloud of 3D points to a triangular mesh. This process approximates a discrete point cloud by a continuous/smooth surface depending on the input data and the applications of users. In this paper, we propose a complete method to reconstruct an elevation surface from 3D point clouds. The method consists of three steps. In the first step, we triangulate an elevation surface of 3D point cloud structured in a 3D grid. In the second step, we remove the outward triangles to deal with concave regions on the boundary of the triangular mesh. In the third step, we reconstruct this surface by filling the hole of triangular mesh. Our method could process very fast for triangulating the surface, preserve the topology and characteristic of the input surface after reconstruction.
Surface reconstruction
Triangle mesh
Elevation (ballistics)
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Point-sampled models generated by 3D-scanning devices are invariably noisy. It is necessary to remove the inevitable noise before realizing surface reconstruction while preserving the underlying surface features in computer graphics. But the point cloud will be incomplete after denoising. In this paper, a novel and surface reconstruction method for noisy point cloud is proposed. First, the noises of the point cloud are deleted by robust ellipsoid criterion, then a surface reconstruction method based on neural network is used to reconstruct surface.
Surface reconstruction
Ellipsoid
3D Reconstruction
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We present a method for reconstructing 3D surface as triangular meshes from imagery. The surface reconstruction requires 3D point cloud for composing vertices of triangle meshes. A standard approach uses incremental structure from motion (SfM) to obtain camera poses and sparse 3D point cloud that are given based on 2D key-point matching. As the 3D surface directly reconstructed from the sparse 3D point cloud often lack detail of objects, multiple-view stereo (MVS) is commonly used to generate dense 3D point cloud. A known problem with the densification is that MVS generates many small patches even for planar flat objects that degrade the quality of surface model. Using dense 3D point cloud also requires high memory capacity for visualization. In this work, we propose to reconstruct 3D surface using sparse 3D point cloud generated by SfM and 3D line segments (3D line cloud) computed from multiple views since these two elements can complement well for representing man-made structures. The proposed method extends the tetrahedra-carving method as it can use 3D point-and-line cloud under the global optimization framework. We demonstrate that the proposed method can efficiently produce surface models whose quality are at least as good as the baseline method using dense 3D point cloud.
Surface reconstruction
3D Reconstruction
Line (geometry)
Structure from Motion
Solid modeling
3D modeling
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The advancement of measurement equipment has enabled the extension of measurement applications from physical samples to the vast and scattered point cloud data. This expansion has fostered the growth of reverse engineering technology, which is widely used in the manufacturing industry. Reverse engineering involves data acquisition and surface reconstruction, where normal vector control is a more straightforward approach than B-spline control polygons for surface design and modification. This study proposed a complex surface reconstruction algorithm incorporating normal vector constraints for reconstructing scattered point clouds. Physical experiments were conducted to demonstrate the feasibility of the proposed method, and the results showed that it has significant potential for detecting complex surfaces.
Surface reconstruction
Reverse engineering
Normal
Spline (mechanical)
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During the course of 3d surface reconstruction,there are a large number of noises and isolated 3d points in raw 3d point clouds,which obtained from images.If we directly use these data to reconstruct surface,the algorithm will make surface sharply prominent and ineffective reconstruction.Because of above problems,a method that sieving 3d point clouds based on DBSCAN is presented in this paper,and then 3d surface is reconstructed using filtered 3d point clouds.Experiments show that good 3d surface reconstruction is obtained using this algorithm.
Surface reconstruction
3D Reconstruction
DBSCAN
3d model
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The reconstruction of real-world surfaces is on high demand in various applications.Most existing reconstruction approaches apply 3D scanners for creating point clouds which are generally sparse and of low density.These points clouds will be triangulated and used for visualization in combination with surface normals estimated by geometrical approaches.However, the quality of the reconstruction depends on the density of the point cloud and the estimation of the surface normals.In this paper, we present a novel deep learning architecture for point cloud upsampling that enables subsequent stable and smooth surface reconstruction.A noisy point cloud of low density with corresponding point normals is used to estimate a point cloud with higher density and appendant point normals.To this end, we propose a compound loss function that encourages the network to estimate points that lie on a surface including normals accurately predicting the orientation of the surface.Our results show the benefit of estimating normals together with point positions.The resulting point cloud is smoother, more complete, and the final surface reconstruction is much closer to ground truth.
Surface reconstruction
Upsampling
Normal
Ground truth
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Citations (13)
This paper achieves optimal 3D point cloud reconstruction based on the specific experiment and concrete actions, and the reconstruction results realistically reflect the real object. We introduce a pipeline for surface reconstruction, including K-nearest neighbor method for point cloud data de-noising, Poisson-disk sampling to simplify the point cloud data, k-nearest neighbor method for normal estimation and Poisson reconstruction to achieve triangular mesh reconstruction of the point cloud data. In the specific operation, we apply an algorithm and select the suitable parameters through our experience at each step, so as to achieve optimal reconstruction results.
Surface reconstruction
3D Reconstruction
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Citations (5)
Structured light-based 3D reconstruction technology has emerged as a focal point of research in the field of 3D modeling due to its exceptional speed and accuracy. However, occlusion often occurs due to the limited field of view between the camera and the projector, as well as the complex surface of the object. Consequently, defects and holes are generated in the reconstructed model, impairing the model's reconstruction quality. In this study, we propose a hole repair method based on a triangular mesh that yields superior repaired effects. Our method effectively addresses the deficiencies in point cloud caused by occlusions. The difference in model accuracy between the repaired point cloud and the original point cloud is 3.618%.
Surface reconstruction
Light Field
Triangle mesh
Structured Light
3D Reconstruction
3d model
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Abstract. The point cloud generated by multiple image matching is classified as an unstructured point cloud because it is not regularly point spaced and has multiple viewpoints. The surface reconstruction technique is used to generate mesh model using unstructured point clouds. In the surface reconstruction process, it is important to calculate correct surface normals. The point cloud extracted from multi images contains position and color information of point as well as geometric information of images used in the step of point cloud generation. Thus, the surface normal estimation based on the geometric constraints is possible. However, there is a possibility that a direction of the surface normal is incorrectly estimated by noisy vertical area of the point cloud. In this paper, we propose an improved method to estimate surface normals of the vertical points within an unstructured point cloud. The proposed method detects the vertical points, adjust their normal vectors by analyzing surface normals of nearest neighbors. As a result, we have found almost all vertical points through point type classification, detected the points with wrong normal vectors and corrected the direction of the normal vectors. We compared the quality of mesh models generated with corrected surface normals and uncorrected surface normals. Result of comparison showed that our method could correct wrong surface normal successfully of vertical points and improve the quality of the mesh model.
Normal
Surface reconstruction
Position (finance)
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Citations (4)