A Framework for Implicit Surfaces Reconstruction form Large Clouds of Points

In reverse engineering, the point cloud data processing is the key, it will directly affect the quality and efficiency of the subsequent reconstruction of the surface. However, the amount of data obtained by three-dimensional laser scanning is often larger and redundant, which will increase the difficulty of the surface reconstruction, therefore, the point cloud data is needed to streamline. Depth analysis of the efficiency and accuracy of curvature grid method, a secondary streamlined algorithm is proposed, which take use of non-uniform grid streamline law based on octree to streamline to the point cloud, and re-use curvature streamline method based on B-spline surface fitting to streamline it. This method, whose streamline ratio is relatively high, can effectively retain the feature point cloud data, providing a great convenience for the follow-up of reverse engineering.

Sharp features of 3D point clouds play an important role in many geometric computations and modeling application. In this paper, a novel modified Partition of Unity (PoU) Based Sharp feature extraction algorithm is proposed, which is directly operated on discrete point clouds. For every point in target point cloud, spherical neighborhood with radius δ is acquired with the help of KD-Tree and weighted average position of points within the δ -neighborhood is computed using modified PoU method. Distance which is the projection of the displace between original point and its Weighted average position along normal direction is defined as the criteria for a point belong to sharp feature or crease line. Experiments on both synthetic data and practical scanner point clouds indicate that our algorithm are both efficient and effective to the task of sharp feature extraction from point clouds. Our method is easy to be implemented and more sensitive to sharp features as well as its low computational complexity.

Surface reconstruction from point clouds is vital for 3D computer vision. State-of-the-art methods leverage large datasets to first learn local context priors that are represented as neural network-based signed distance functions (SDFs) with some parameters encoding the local contexts. To reconstruct a surface at a specific query location at inference time, these methods then match the local reconstruction target by searching for the best match in the local prior space (by optimizing the parameters encoding the local context) at the given query location. However, this requires the local context prior to generalize to a wide variety of unseen target regions, which is hard to achieve. To resolve this issue, we introduce Predictive Context Priors by learning Predictive Queries for each specific point cloud at inference time. Specifically, we first train a local context prior using a large point cloud dataset similar to previous techniques. For surface reconstruction at inference time, however, we specialize the local context prior into our Predictive Context Prior by learning Predictive Queries, which predict adjusted spatial query locations as displacements of the original locations. This leads to a global SDF that fits the specific point cloud the best. Intuitively, the query prediction enables us to flexibly search the learned local context prior over the entire prior space, rather than being restricted to the fixed query locations, and this improves the generalizability. Our method does not require ground truth signed distances, normals, or any additional procedure of signed distance fusion across overlapping regions. Our experimental results in surface reconstruction for single shapes or complex scenes show significant improvements over the state-of-the-art under widely used benchmark-s. Code and data are available at https://github.com/mabaorui/PredictableContextPrior.

The use of laser scanning techniques has become a common way to measure the real world. Millions of points could be generated by this system, which brings forth the problem of visualizing and eventually performing analyses on them. In the open source domain, various software packages exist to visualize 3D point clouds. However, most of these software packages do not allow the real-time rendering of large point clouds. Few of them also allow visualization in an immersive manner. The research aims to create an open source viewer for large 3D point clouds, which enables a dynamic and immersive visualization. In order to do so, rendering and point cloud management strategies must be implemented to avoid overloading the computer’s memory. The rendering is done using OpenGL engine by utilizing the graphic card’s memory in order to perform faster visualization. The program consists of a pre-processing stage in which the point cloud files are divided into quadtrees and then subsampled using the random tree sampling method. The visualization itself will calculate the distance between the point of view and the center of nodes generated by the pre-processing stage. The amount of points rendered within each node will depend on this distance; the farther away the node is from the point of view, the fewer points are rendered. A loading and unloading function enables the point cloud to be rendered dynamically. With the point cloud management algorithm implemented, the resulting program is able to load large point clouds generated by a mobile laser scanner using an ordinary computer. The resulting program as well as the source code will be available for the public due to its open source nature.

Background: Implicit surface is a kind of surface modeling tool, which is widely used in point cloud reconstruction, deformation and fusion due to its advantages of good smoothness and Boolean operation. The most typical method is the surface reconstruction with Radial Basis Functions (RBF) under normal constraints. RBF has become one of the main methods of point cloud fitting because it has a strong mathematical foundation, an advantage of computation simplicity, and the ability of processing nonuniform points. Objective: Techniques and patents of implicit surface reconstruction interpolation with RBF are surveyed. Theory, algorithm, and application are discussed to provide a comprehensive summary for implicit surface reconstruction in RBF and Hermite Radial Basis Functions (HRBF) interpolation. Methods: RBF implicit surface reconstruction interpolation can be divided into RBF interpolation under the constraints of points and HRBF interpolation under the constraints of points and corresponding normals. Results: A total of 125 articles were reviewed, in which more than 30% were related to RBF in the last decade. The continuity properties and application fields of the popular global supported radial basis functions and compactly supported radial basis functions are analyzed. Different methods of RBF and HRBF implicit surface reconstruction are evaluated, and the challenges of these methods are discussed. Conclusion: In future work, implicit surface reconstruction via RBF and HRBF should be further studied in fitting accuracy, computation speed, and other fundamental problems. In addition, it is a more challenging but valuable research direction to construct a new RBF with both compact support and improved fitting accuracy.

With the increase of point cloud scale, the time required by traditional ICP-related point cloud registration methods increases dramatically, which cannot meet the registration requirements of large-scale point clouds. In this paper, a fast registration technique for large scale point clouds based on virtual viewpoint image generation is studied. Firstly, the projection image of color point cloud is generated by virtual viewpoint. Then, the feature is extracted based on ORB and the rotation and translation matrix is calculated. The experimental results show that the registration time of the proposed method is about 1s when the size of the point cloud is from 300,000 to 2 million, which is improved by 17–258 times compared with the traditional ICP registration method, and the registration error is reduced by 80% from ICP 5.0 to 1.0. This paper provides a new idea and method for large-scale color point cloud registration.

Terrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. Obtaining the congruent set of tuples in a large point cloud scene can be challenging. To address this concern, we propose a registration method based on the voxel grid of the point cloud in this paper. First, we establish a voxel grid structure and index structure for the point cloud and eliminate uneven point cloud density. Then, based on the point cloud distribution in the voxel grid, keypoints are calculated to represent the entire point cloud. Fast query of voxel grids is used to restrict the selection of calculation points and filter out 4-point tuples on the same surface to reduce ambiguity in building registration. Finally, the voxel grid is used in our proposed approach to perform random queries of the array. Using different indoor and outdoor data to compare our proposed approach with other 4-point congruent set methods, according to the experimental results, in terms of registration efficiency, the proposed method is more than 50% higher than K4PCS and 78% higher than Super4PCS.

Extracting roads from dense point clouds in large scale urban environment

GrainPointNet: A deep-learning framework for non-invasive sorghum panicle grain count phenotyping

Abstract. Utilizing ground control points (GCPs) to georeference photogrammetry-based point cloud data is a common practice in unmanned aerial system (UAS) mapping. Direct georeferencing or integrated sensor orientation (ISO) can be used to obtain georeferenced point clouds from UAS without relying heavily on GCPs. However, the accuracy of the point cloud may be impacted by the accuracy of the trajectory solution obtained by GNSS. To improve point cloud accuracy, post-processing kinematic (PPK) solutions can be applied to the UAS trajectory, which may provide higher accuracy than low-accuracy trajectory solutions and minimize the reliance on GCPs. This study compares the accuracy and precision of two different point clouds generated using different methods. One point cloud was generated using traditional photogrammetric methods with low accuracy Global Navigation Satellite System (GNSS) observations from the UAS and GCPs that have an average accuracy of one to two centimeters, while the other was generated using PPK trajectory solution for the UAS’s trajectory with two software: open-source Emlid Studio and the widely used Inertial Explorer. The use of PPK techniques in UAS mapping may have several potential benefits over traditional methods. By correcting the errors in the UAS's trajectory, a user may only need to depend on fewer ground control points, which can reduce the time and cost associated with fieldwork. This is particularly useful in areas that are difficult to access or have limited ground control point options, such as in urban or forested areas. To evaluate performance, a GNSS receiver is used to obtain measurements on checkpoints, which are used to assess the accuracies of the point clouds. In our experiments, the accuracy of the point clouds generated using PPK trajectory solution with high accuracy GCPs was found to be higher than those generated with low accuracy GNSS observations while aided with high accuracy ground control points. While the use of PPK with GCPs is generally expected to provide more accurate and reliable data than low-accuracy GNSS observations even after adjusting with GCPs, the number and distribution of GCPs can still significantly impact overall accuracy. Therefore, careful consideration of the number of GCPs and their placement is essential to achieve the desired level of efficiency and effectiveness in UAS mapping.

Suppose a large and dense point cloud of an object with complex geometry is available that can be approximated by a smooth univariate function. In general, for such point clouds the “best” approximation using the method of least squares is usually hard or sometimes even impossible to compute. In most cases, however, a “near-best” approximation is just as good as the “best”, but usually much easier and faster to calculate. Therefore, a fast approach for the approximation of point clouds using Chebyshev polynomials is described, which is based on an interpolation in the Chebyshev points of the second kind. This allows to calculate the unknown coefficients of the polynomial by means of the Fast Fourier transform (FFT), which can be extremely efficient, especially for high-order polynomials. Thus, the focus of the presented approach is not on sparse point clouds or point clouds which can be approximated by functions with few parameters, but rather on large dense point clouds for whose approximation perhaps even millions of unknown coefficients have to be determined.

Point cloud is emerged as a promising media format to represent realistic 3D objects or scenes in applications, such as virtual reality, teleportation, etc. How to accurately quantify the subjective point cloud quality for application-driven optimization, however, is still a challenging and open problem. In this paper, we attempt to tackle this problem in a systematic means. First, we produce a fairly large point cloud dataset where ten popular point clouds are augmented with seven types of impairments (e.g., compression, photometry/color noise, geometry noise, scaling) at six different distortion levels, and organize a formal subjective assessment with tens of subjects to collect mean opinion scores (MOS) for all 420 processed point cloud samples (PPCS). We then try to develop an objective metric that can accurately estimate the subjective quality. Towards this goal, we choose to project the 3D point cloud onto six perpendicular image planes of a cube for the color texture image and corresponding depth image, and aggregate image-based global (e.g., Jensen-Shannon (JS) divergence) and local features (e.g., edge, depth, pixel-wise similarity, complexity) among all projected planes for a final objective index. Model parameters are fixed constants after performing the regression using a small and independent dataset previously published. The proposed metric has demonstrated the state-of-the-art performance for predicting the subjective point cloud quality compared with multiple full-reference and no-reference models, e.g., the weighted peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM) and natural image quality evaluator (NIQE). The dataset is made publicly accessible at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://smt.sjtu.edu.cn</uri> or <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://vision.nju.edu.cn</uri> for all interested audiences.

Topological Data Analysis is a machine learning method that summarizes the topological features of a space. Persistent Homology (PH) can identify these topological features as they persist within a point cloud; persisting in respect to the connectedness of the point cloud at increasing distances. The utility of PH is apparent in several fields including bioinformatics, network security, and object classification. However, the memory complexity of PH limits the application to relatively small point clouds for low-dimensional topological feature identification. For this reason, numerous approaches to optimize and approximate the PH have been introduced for providing results over large point clouds. One solution, Partitioned Persistent Homology (PPH), has shown favorable approximation on a single node with significant performance improvement. However, the single-node approach is limited by the available system memory, leading to the need for a distributed approach for additional (especially memory) resources. This paper studies a distributed version of PPH for use with large point clouds over a high-performance compute cluster. Experimental results of the distributed algorithm against previous studies is presented along with scalability of the distributed library.

Dense photorealistic point clouds can depict real-world dynamic objects in high resolution and with a high frame rate. Frame interpolation of such dynamic point clouds would enable the distribution, processing, and compression of such content. In this work, we propose a first point cloud interpolation framework for photorealistic dynamic point clouds. Given two consecutive dynamic point cloud frames, our framework aims to generate intermediate frame(s) between them. The proposed deep learning framework has three major components: the encoder module, the fusion network, and the multi-scale point cloud synthesis module. The encoder module extracts multi-scale features from two consecutive frames. The fusion network employs a novel 4D feature learning technique to merge the multi-scale features from consecutive frames. Finally, the multi-scale point cloud synthesis module hierarchically reconstructs the interpolated point cloud intermediate frame at different resolutions. We evaluate our framework on high-resolution point cloud datasets used in MPEG, JPEG Pleno, and AVS standards. The quantitative and qualitative results demonstrate the effectiveness of the proposed method.

With the continuous improvement of living standards in modern society, the demand for refined 3D object representation models has grown considerably.Concurrently, the diversification of methods for acquiring 3D point clouds has significantly enhanced our ability to digitally express the surrounding environment.Consequently, the efficient and straightforward generation of high-precision 3D mesh models from point clouds has become particularly vital.As a typical production method for 3D mesh models, point cloud surface reconstruction technology has gained widespread application across various industries owing to its convenience and efficiency.Therefore, in this paper, we propose a distance-constrained scale-space surface reconstruction algorithm, which utilizes the neighborhood distances of point clouds to constrain the anomalous triangular mesh generated by the scale-space reconstruction algorithm, thereby enabling the more precise and efficient surface reconstruction of point clouds.Initially, the raw point set obtained from 3D laser scanning undergoes preprocessing, followed by point cloud reconstruction and mesh construction using the distance-constrained scale-space algorithm.Finally, the constructed mesh is back-projected onto the original point set, resulting in a more refined 3D model.Experiments conducted on the urban underground tunnel dataset successfully reconstructed the surface model of the tunnels, validating the effectiveness of the proposed method and enhancing the visual quality of the model.