Poisson Surface Reconstruction Research Articles

Enhanced Interactive Rendering for Rovers of Lunar Polar Region and Martian Surface

Appropriate environmental sensing methods and visualization representations are crucial foundations for the in situ exploration of planets. In this paper, we developed specialized visualization methods to facilitate the rover’s interaction and decision-making processes, as well as to address the path-planning and obstacle-avoidance requirements for lunar polar region exploration and Mars exploration. To achieve this goal, we utilize simulated lunar polar regions and Martian environments. Among them, the lunar rover operating in the permanently shadowed region (PSR) of the simulated crater primarily utilizes light detection and ranging (LiDAR) for environmental sensing; then, we reconstruct a mesh using the Poisson surface reconstruction method. After that, the lunar rover’s traveling environment is represented as a red-green-blue (RGB) image, a slope coloration image, and a theoretical water content coloration image, based on different interaction needs and scientific objectives. For the rocky environment where the Mars rover is traveling, this paper enhances the display of the rocks on the Martian surface. It does so by utilizing depth information of the rock instances to highlight their significance for the rover’s path-planning and obstacle-avoidance decisions. Such an environmental sensing and enhanced visualization approach facilitates rover path-planning and remote–interactive operations, thereby enabling further exploration activities in the lunar PSR and Mars, in addition to facilitating the study and communication of specific planetary science objectives, and the production and display of basemaps and thematic maps.

Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction

Surface reconstruction from raw point clouds has been studied for decades in the computer graphics community, which is highly demanded by modeling and rendering applications nowadays. Classic solutions, such as Poisson surface reconstruction, require point normals as extra input to perform reasonable results. Modern transformer-based methods can work without normals, while the results are less fine-grained due to limited encoding performance in local fusion from discrete points. We introduce a novel normalized matrix attention transformer (Tensorformer) to perform high-quality reconstruction. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">matrix attention</i> allows for simultaneous point-wise and channel-wise message passing, while the previous vector attention loses neighbor point information across different channels. It brings more degree of freedom in feature learning and thus facilitates better modeling of local geometries. Our method achieves state-of-the-art on two commonly used datasets, ShapeNetCore and ABC, and attains 4% improvements on IOU on ShapeNet. Our implementation will be released upon acceptance.

Research and Application of Urban 3D Modeling Technology in Virtual Reality Scenes

Abstract In this paper, an automatic building extraction process based on MVS point clouds is proposed to automatically extract building point clouds from urban MVS dense point clouds of complex scenes by projection, morphological expansion and contour extraction techniques. Aiming at the deficiency of Poisson surface reconstruction, this paper proposes a surface model optimization method based on RANSAC fast fitting. The method generates the optimized surface model through the filter denoising process and chunked RANSAC fast fitting. Finally, a workflow for the 3D reconstruction of urban buildings based on the MVS point cloud is proposed. In the analysis for the urban 3D modeling technique, the average error of the model after reconstruction is only 0.731%, and the measurement errors in the three-dimensional directions of length, width, and height are less than 5 cm. and the time consumed before and after the optimized method in this paper is reduced by an average of 3.09 s. Therefore, this study provides a simple and efficient method for the automatic extraction and 3D reconstruction of urban buildings.

Poisson Manifold Reconstruction — Beyond Co‐dimension One

AbstractScreened Poisson Surface Reconstruction creates 2D surfaces from sets of oriented points in 3D (and can be extended to co‐dimension one surfaces in arbitrary dimensions). In this work we generalize the technique to manifolds of co‐dimension larger than one. The reconstruction problem consists of finding a vector‐valued function whose zero set approximates the input points. We argue that the right extension of screened Poisson Surface Reconstruction is based on exterior products: the orientation of the point samples is encoded as the exterior product of the local normal frame. The goal is to find a set of scalar functions such that the exterior product of their gradients matches the exterior products prescribed by the input points. We show that this setup reduces to the standard formulation for co‐dimension 1, and leads to more challenging multi‐quadratic optimization problems in higher co‐dimension. We explicitly treat the case of co‐dimension 2, i.e., curves in 3D and 2D surfaces in 4D. We show that the resulting bi‐quadratic problem can be relaxed to a set of quadratic problems in two variables and that the solution can be made effective and efficient by leveraging a hierarchical approach.

Alternately denoising and reconstructing unoriented point sets

We propose a new strategy to bridge point cloud denoising and surface reconstruction by alternately updating the denoised point clouds and the reconstructed surfaces. In Poisson surface reconstruction, the implicit function is generated by a set of smooth basis functions centered at the octnodes. When the octree depth is properly selected, the reconstructed surface is a good smooth approximation of the noisy point set. Our method projects the noisy points onto the surface and alternately reconstructs and projects the point set. We use the iterative Poisson surface reconstruction (iPSR) to support unoriented surface reconstruction. Our method iteratively performs iPSR and acts as an outer loop of iPSR. Considering that the octree depth significantly affects the reconstruction results, we propose an adaptive depth selection strategy to ensure an appropriate depth choice. To manage the oversmoothing phenomenon near the sharp features, we propose a λ-projection method, which means to project the noisy points onto the surface with an individual control coefficient λi for each point. The coefficients are determined through a Voronoi-based feature detection method. Experimental results show that our method achieves high performance in point cloud denoising and unoriented surface reconstruction within different noise scales, and exhibits well-rounded performance in various types of inputs. The source code is available at https://github.com/Submanifold/AlterUpdate.

Research on 3D reconstruction method of cattle face based on image

As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features.

Point normal orientation and surface reconstruction by incorporating isovalue constraints to Poisson equation

Oriented normals are common pre-requisites for many geometric algorithms based on point clouds, such as Poisson surface reconstruction. However, it is not trivial to obtain a consistent orientation. In this work, we bridge orientation and reconstruction in the implicit space and propose a novel approach to orient point cloud normals by incorporating isovalue constraints to the Poisson equation. In implicit surface reconstruction, the reconstructed shape is represented as an isosurface of an implicit function defined in the ambient space. Therefore, when such a surface is reconstructed from a set of sample points, the implicit function values at the points should be close to the isovalue corresponding to the surface. Based on this observation and the Poisson equation, we propose an optimization formulation that combines isovalue constraints with local consistency requirements for normals. We optimize normals and implicit functions simultaneously and solve for a globally consistent orientation. Thanks to the sparsity of the linear system, our method can work on an average laptop with reasonable computational time. Experiments show that our method can achieve high performance in non-uniform and noisy data and manage varying sampling densities, artifacts, multiple connected components, and nested surfaces. The source code is available at https://github.com/Submanifold/IsoConstraints.

Detection of geometric properties of discontinuities on the Špičunak rock slope (Croatia) using high-resolution 3D Point Cloud generated from Terrestrial Laser Scanning

Rock mass characterization is a very important part of engineering geological investigation. For a better understanding of the rock mass behaviour, it is crucially important to obtain as much as possible information about the discontinuity network, especially about orientation and the number of dominant discontinuity sets. The traditional methodology includes field mapping which dominantly produces a limited amount of data and consequently only a rough estimate about discontinuity network. To increase the number of measurements and to eliminate orientation bias, rock mass on the Špičunak rock slope in Gorski kotar, Croatia, was analysed using a combination of 3D Point Cloud and Textured Mesh Model generated from 3D Point Cloud by Poisson surface reconstruction. Both models were obtained from Terrestrial Laser Scanning. Two considerably different parts of a rock slope, with different weathering conditions and different degrees of fracturing were mapped. Discontinuities were mapped in the field and on the models using manual mapping techniques and semi-automated methods. Manual mapping on a 3D Point Cloud and Textured Mesh Model was done by Compass plugin and by Trace a polyline tool in Cloud Compare software version V2.12 and semi-automated mapping methods were done by Discontinuity Set Extractor and qFacet Fast Marching plugin for Cloud Compare software version V2.12. This study was used to show how the application of different methodologies, for the detection of geometric properties of discontinuities, influences the result. Statistical analyses were performed on the collected data to determine differences in the accuracy between the mapping techniques. Manual mapping on the 3D Point Cloud and high-resolution Textured Mesh Model showed good agreement with field measurements, apart from the higher number of discontinuities mapped by remote sensing methods. On the other hand, significant deviations were found between manual and semi-automated mapping techniques. Semiautomated methods did not correctly detect certain discontinuities, especially bedding planes that are perpendicular to a rock face. Also, semi-automated methods overestimate the number of discontinuity sets, especially in a highly weathered and highly fractured rock mass. These differences between methods can influence kinematic analysis results. Based on the results, an appropriate methodology was proposed to utilize the advantages of both manual and semiautomated methods. The proposed approach presents a powerful tool to accurately map and measure discontinuity orientation with results comparable to the field measurements.

Parameter Extraction and Performance Analysis of 3D Surface Reconstruction Techniques

Digital image-based 3D surface reconstruction is a streamlined and proper means of studying the features of the object being modelled. The generation of true 3D content is a very crucial step in any 3D system. A methodology to reconstruct a 3D surface of objects from a set of digital images is presented in this paper. It is simple, robust, and can be freely used for the construction of 3D surfaces from images. Digital images are taken as input to generate sparse and dense point clouds in 3D space from the detected and matched features. Poisson Surface, Ball Pivoting, and Alpha shape reconstruction algorithms have been used to reconstruct photo-realistic surfaces. Various parameters of these algorithms that are critical to the quality of reconstruction are identified and the effect of these parameters with varying values is studied. The results presented in this study will give readers an insight into the behaviour of various algorithmic parameters with computation time and fineness of reconstruction.

An Improved Poisson Surface Reconstruction Algorithm based on the Boundary Constraints

The usage of the point cloud surface reconstruction to generate high-precision 3D models has been widely applied in various fields. In order to deal with the problems of insufficient accuracy, pseudo-surfaces and high time cost caused by the traditional surface reconstruction algorithms of the point cloud data, this paper proposes an improved Poisson surface reconstruction algorithm based on the boundary constraints. For large density point cloud data obtained from 3D laser scanning, the proposed method firstly uses an octree instead of the KD-tree to search the near neighborhood; then, it uses the Open Multi-Processing (OpenMP) to accelerate the normal estimation based on the moving least squares algorithm; meanwhile, the least-cost spanning tree is employed to adjust the consistency of the normal direction; and finally a screened Poisson algorithm with the Neumann boundary constraints is proposed to reconstruct the point cloud. Compared with the traditional methods, the experiments on three open datasets demonstrated that the proposed method can effectively reduce the generation of pseudo-surfaces. The reconstruction time of the proposed algorithm is about 16% shorter than that of the traditional Poisson reconstruction algorithm, and produce better reconstruction results in the term of quantitative analysis and visual comparison.

Stochastic Poisson Surface Reconstruction

We introduce a statistical extension of the classic Poisson Surface Reconstruction algorithm for recovering shapes from 3D point clouds. Instead of outputting an implicit function, we represent the reconstructed shape as a modified Gaussian Process, which allows us to conduct statistical queries (e.g., the likelihood of a point in space being on the surface or inside a solid). We show that this perspective: improves PSR's integration into the online scanning process, broadens its application realm, and opens the door to other lines of research such as applying task-specific priors.

SCAN-VS-BIM FOR REAL-TIME PROGRESS MONITORING OF BRIDGE CONSTRUCTION PROJECT

Abstract. New transport infrastructure construction can stimulate the growth of economy as well as improving the public citizen welfare. However, with the rising number of mega infrastructure projects, the low project performance, such as project delay and cost escalation, are challenging the traditional Architecture, Engineering, and Construction (AEC) industry. Traditional Construction progress monitoring methods rely on manual data collecting and paperwork reporting which can be labor-intensive, time-consuming and error-prone. Therefore, it is necessary for the involved stakeholders to introduce advanced technologies which facilitates assessing the construction performance automatically and ensures the projects to be delivered on time. The application of building information modeling (BIM) provides involved parties an accurate understandable single source of truth that can improve the interoperability of project information. Nevertheless, current ‘Scan-to-BIM’ workflow cannot support the demand for real-time data analysis and status reporting. This paper presents a semi-automatic construction progress monitoring framework that evaluating the project performance of the infrastructure in real-time. It introduces Hausdorff distance which transmits the 3-D geometrical information contained by as-built point cloud to virtual point cloud directly, to avoid the drawbacks of space partitioning algorithms. The Poisson surface reconstruction utilizing volume as criterion to improve the robustness of progress determination. In addition, the application of 2D polygon fitting provides a potentially feasible method to identify the installation of pre-cast components of the bridge construction. The results indicate that the proposed framework can effectively monitor the geometric increment of road bridge construction project.

Iterative poisson surface reconstruction (iPSR) for unoriented points

Poisson surface reconstruction (PSR) remains a popular technique for reconstructing watertight surfaces from 3D point samples thanks to its efficiency, simplicity, and robustness. Yet, the existing PSR method and subsequent variants work only for oriented points. This paper intends to validate that an improved PSR, called iPSR, can completely eliminate the requirement of point normals and proceed in an iterative manner. In each iteration, iPSR takes as input point samples with normals directly computed from the surface obtained in the preceding iteration, and then generates a new surface with better quality. Extensive quantitative evaluation confirms that the new iPSR algorithm converges in 5--30 iterations even with randomly initialized normals. If initialized with a simple visibility based heuristic, iPSR can further reduce the number of iterations. We conduct comprehensive comparisons with PSR and other powerful implicit-function based methods. Finally, we confirm iPSR's effectiveness and scalability on the AIM@SHAPE dataset and challenging (indoor and outdoor) scenes. Code and data for this paper are at https://github.com/houfei0801/ipsr.

Terrestrial Laser Scanning to Assess Eucalyptus Aboveground Biomass: A Surface Reconstruction Approach

Terrestrial laser scanners (TLS) provide an alternative to traditional field sampling for gathering accurate volumetric data about vegetations without destroying the tree samples. However, popular volumetric modeling approaches (e.g., those cylindrical methods) oversimplify vegetation structure by underutilizing surface undulations provided by the point cloud. Thus, this study aimed to test the capability of two specialized surface reconstruction methods against a traditional cylindrical method to obtain the stem volumes and masses from 3D-cloud points generated by terrestrial laser scanners under relatively controlled conditions in a eucalyptus plantation in Muang, Nakornratchasima Province, Thailand. The TLS point data were collected from the test plots, and then three algorithms, the Poisson Surface Reconstruction (PSR), the Screen Poisson Surface Reconstruction (SPSR), and the traditional Quantitative Structure Model (QSM) were applied in order to build volumetric models of the sample eucalyptus trees. It is notable that this is the first study to test the SPSR method on real tree samples (N=40). The results were then compared with the reference values measured by a water replacement method. The root means square errors (RMSE) were estimated between the xylometric referenced aboveground biomass (AGB) and the TLS three methods. The SPSR approach yielded the most accurate results (RMSE of 0.49 kg or 6.91%), while the PSR method resulted in an RMSE of 0.60 kg (8.37%). The QSM method had the worst results, with an RMSE of 1.09 kg (15.31%). Despite the occlusion problem that caused 20% systematic error, this outcome provides evidence that the use of two Poisson reconstruction methods (e.g., PSR and SPSR) provides effective alternatives to accurately quantify aboveground biomass for eucalyptus trees.

Thickness measurement method for the thermal protection layer of a solid rocket motor based on a laser point cloud

The thermal protection layer of the solid rocket motor (SRM) is an important elastic material applied to the adhesive shell and propellant; the thickness of the layer is related to the performance of the SRM. This paper proposes a method of linear laser scanning to realise the precise thickness measurement of the thermal protection layer. The custom-designed data acquisition device separately collects distance values from the inner surface of the thermal protection layer, before and after coating, using a linear laser sensor. The two measured distance values are subtracted and D-H modelling (a classical kinematics modelling method proposed by Denavit and Hartenberg) is conducted, then the system errors are analysed and calibrated to obtain the 3D point cloud data of the profile. The iterative closest point (ICP) algorithm is used to register the point cloud data relating to the internal surface, then an octree-based simplification method is proposed to realise the denoising and simplification of the point cloud data. Next, the normal vector of the point cloud data is calculated and the reference surface is extracted as the projection plane. The simplified point cloud is then projected onto the plane and the geometric information of the point cloud data after projection is extracted. A series of distance differences, namely the thickness of the thermal protection layer, is obtained. Finally, the Poisson surface reconstruction algorithm is used to display the inner surface of the thermal protection layer. The results show that the difference between the calculated values and the measured values is less than 0.1 mm, indicating that the thickness of the thermal protection layer of the SRM can be measured by the proposed non-contact method.

Terrestrial laser scanning for 3D archaeological documentation. The prehistoric Cave of Sa Miniera de Santu Josi (Sardinia, Italy)

Abstract Caves have a great archaeological importance: they were used as a dwelling, as a shelter of animals, as an occasional refuge both for funerary and religious purposes. A cave survey is the first step towards their exploration. This study describes the San Giorgio cave’s survey that is the object of an archaeological research that concerns both the area above it and the underground environments. The cave, located in the north-west of Sardinia, has an extension of about 140 meters and has a maximum depth (surveyed) of -15 meters. Sixty-two TLS scans were carried out producing 1.5 milliard points. The Poisson surface reconstruction algorithm [3] is used to produce the 3D Model. A 3D model in low resolution can be adopted for aims of public archaeology; however archaeologists should take advantage of all the information available in the original point cloud.

3D Printed hair modeling from strand-level hairstyles

Recent advances in the design and fabrication of personalized figurines have made the creation of high-quality figurines possible for ordinary users with the facilities of 3D printing techniques. The hair plays an important role in gaining the realism of the figurines. Existing hair reconstruction methods suffer from the high demand for acquisition equipment, or the result is approximated very coarsely. Instead of creating hairs for figurines by scanning devices, we present a novel surface reconstruction method to generate a 3D printable hair model with geometric features from a strand-level hairstyle, thus converting the exiting digital hair database to a 3D printable database. Given a strand-level hair model, we filter the strands via bundle clustering, retain the main features, and reconstruct hair strands in two stages. First, our algorithm is the key to extracting the hair contour surface according to the structure of strands and calculating the normal for each vertex. Next, a close, manifold triangle mesh with geometric details and an embedded direction field is achieved with the Poisson surface reconstruction. We obtain closed-manifold hairstyles without user interactions, benefiting personalized figurine fabrication. We verify the feasibility of our method by exhibiting a wide range of examples.

3D Texture Reconstruction of Abdominal Cavity Based on Monocular Vision SLAM for Minimally Invasive Surgery

The depth information of abdominal tissue surface and the position of laparoscope are very important for accurate surgical navigation in computer-aided surgery. It is difficult to determine the lesion location by empirically matching the laparoscopic visual field with the preoperative image, which is easy to cause intraoperative errors. Aiming at the complex abdominal environment, this paper constructs an improved monocular simultaneous localization and mapping (SLAM) system model, which can more accurately and truly reflect the abdominal cavity structure and spatial relationship. Firstly, in order to enhance the contrast between blood vessels and background, the contrast limited adaptive histogram equalization (CLAHE) algorithm is introduced to preprocess abdominal images. Secondly, combined with AKAZE algorithm, the Oriented FAST and Rotated BRIEF(ORB) algorithm is improved to extract the features of abdominal image, which improves the accuracy of extracted symmetry feature points pair and uses the RANSAC algorithm to quickly eliminate the majority of mis-matched pairs. The medical bag-of-words model is used to replace the traditional bag-of-words model to facilitate the comparison of similarity between abdominal images, which has stronger similarity calculation ability and reduces the matching time between the current abdominal image frame and the historical abdominal image frame. Finally, Poisson surface reconstruction is used to transform the point cloud into a triangular mesh surface, and the abdominal cavity texture image is superimposed on the 3D surface described by the mesh to generate the abdominal cavity inner wall texture. The surface of the abdominal cavity 3D model is smooth and has a strong sense of reality. The experimental results show that the improved SLAM system increases the registration accuracy of feature points and the densification, and the visual effect of dense point cloud reconstruction is more realistic for Hamlyn dataset. The 3D reconstruction technology creates a realistic model to identify the blood vessels, nerves and other tissues in the patient’s focal area, enabling three-dimensional visualization of the focal area, facilitating the surgeon’s observation and diagnosis, and digital simulation of the surgical operation to optimize the surgical plan.

High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner

High-throughput phenotyping involves many samples and diverse trait types. For the goal of automatic measurement and batch data processing, a novel method for high-throughput legume seed phenotyping is proposed. A pipeline of automatic data acquisition and processing, including point cloud acquisition, single-seed extraction, pose normalization, three-dimensional (3D) reconstruction, and trait estimation, is proposed. First, a handheld laser scanner is used to obtain the legume seed point clouds in batches. Second, a combined segmentation method using the RANSAC method, the Euclidean segmentation method, and the dimensionality of the features is proposed to conduct single-seed extraction. Third, a coordinate rotation method based on PCA and the table normal is proposed to conduct pose normalization. Fourth, a fast symmetry-based 3D reconstruction method is built to reconstruct a 3D model of the single seed, and the Poisson surface reconstruction method is used for surface reconstruction. Finally, 34 traits, including 11 morphological traits, 11 scale factors, and 12 shape factors, are automatically calculated. A total of 2500 samples of five kinds of legume seeds are measured. Experimental results show that the average accuracies of scanning and segmentation are 99.52% and 100%, respectively. The overall average reconstruction error is 0.014 mm. The average morphological trait measurement accuracy is submillimeter, and the average relative percentage error is within 3%. The proposed method provides a feasible method of batch data acquisition and processing, which will facilitate the automation in high-throughput legume seed phenotyping.

3D reconstruction of human body model based on incremental Motion recovery structure

3D reconstruction of human body model is a very important research topic in 3D reconstruction and also a challenging research direction in engineering field. In this paper, the whole pipeline flow of 3D reconstruction of human body model based on incremental motion recovery structure is proposed. Use mobile phone to collect images from different angles and screen them; Secondly, feature extraction and matching under SIFT operator, sparse reconstruction of incremental motion recovery structure, dense reconstruction based on depth map and other processes are carried out. Poisson surface reconstruction is finally carried out to achieve model reconstruction. Experiments show that the effect subject of the reconstructed model is clear.