Poisson Surface Reconstruction Research Articles

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.

A Study on the Whole-skin Peeling System of a Snakehead Based on Three-dimensional Reconstruction

HighlightsA 3D reconstruction algorithm is proposed to determine the snakehead area.The whole-skin peeling system of snakehead is designed by a convolutional neural network.An automatic whole-skin peeling device is presented with computer vision technology.Abstract. A whole-skin peeling system for a snakehead is designed in this article. The snakehead photos are taken from multiple angles and are first filtered to extract the image features, before reconstructing the sparse model. Then, the characteristic parameters of the snakehead are determined through the point cloud coordinates, and dense reconstruction is carried out based on sparse points. The accurate three-dimensional spatial coordinates of the snakehead are obtained by the octree algorithm and Poisson surface reconstruction algorithm. Based on the triangular meshing algorithm, the skin area of a snakehead is calculated to determine whether the snakehead meets the peeling conditions. A trained Faster Region Convolutional Neural Network (R-CNN) is used to identify the fin position on the snakehead reconstruction model and program the cutting route on the fish neck, while the cutting route of the fish belly is determined by selecting the area of the excretory hole through the ROI region of interest. Finally, the designed mechanical structure is used to peel the whole skin of the snakehead. The experimental results show that the maximum fluctuation rate of the snakehead area calculated by the proposed three-dimensional reconstruction algorithm is less than 4.0%, and the accuracy reaches 87.3%. The average precision (AP) for the determination method of the fish neck cutting position on the snakehead is 90.89%, which proves the effectiveness and feasibility of this method. The present results provide useful technical support for automating fish whole-skin peeling and shedding light on the accurate determination of the surface area and the extrication of the morphological feature parameters of other organisms. Keywords: Neural network, Point cloud, Snakehead, Skin peeling, Three-dimensional reconstruction.

Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression.

In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.

Algorithm Development for Generation of Powder Particles for Use in Numerical Simulations of Powder Bed Fusion Additive Manufacturing Processes

This article presents an algorithm development methodology for incorporating the effects of powder quality into numerical simulations of powder bed fusion technologies. The framework leverages x-ray microtomography measurements of samples representative of changes in powder quality with powder reuse. Individual particles are labelled and analysed by postprocessing the three-dimensional data. Included in this data is the particle surface normal and point cloud information which can be used to reconstruct the particle using Poisson Surface Reconstruction in Open3D. This reconstruction can then be exported as a Standard Tessellation Language (STL) file that can be incorporated into discrete element method solvers such as YADE or finite element frameworks such as MOOSE. In this sense, libraries of particles can be generated and used in simulations. To demonstrate the applicability of this method, a publicly available NIST dataset is leveraged. In lieu of experimental data, a second method using Computer-Generated Imagery (CGI) software, Blender, is also presented. The methods are able to replicate powder features, such as satellites and surface irregularity, that are functions of both powder manufacturing methods as well as powder reuse cycles. These particle irregularities have important implications on part quality and manufacturing cost.

Multi-Reconstruction from Points Cloud by Using a Modified Vector-Valued Allen–Cahn Equation

The Poisson surface reconstruction algorithm has become a very popular tool of reconstruction from point clouds. If we reconstruct each region separately in the process of multi-reconstruction, then the reconstructed objects may overlap with each other. In order to reconstruct multicomponent surfaces without self-intersections, we propose an efficient multi-reconstruction algorithm based on a modified vector-valued Allen–Cahn equation. The proposed algorithm produces smooth surfaces and closely preserves the original data without self-intersect. Based on operator splitting techniques, the numerical scheme is divided into one linear equation and two nonlinear equations. The linear equation is discretized using an implicit method, and the resulting discrete system of equation is solved by a fast Fourier transform. The two nonlinear equations are solved analytically due to the availability of a closed-form solution. The numerical scheme has merit in that it can be straightforwardly applied to a graphics processing unit, allowing for accelerated implementation that performs much faster than central processing unit alternatives. Various experimental, numerical results demonstrate the effectiveness and robustness of the proposed method.

Robust resistance to noise and outliers: Screened Poisson Surface Reconstruction using adaptive kernel density estimation

Screened Poisson Surface Reconstruction has a good performance among the state-of-art surface reconstruction algorithms in obtaining a triangle mesh from oriented points. In order to better deal with nonuniform point clouds, Screened Poisson Surface Reconstruction uses B-spline functions with a fixed support for kernel density estimation to construct a vector field for solving the screened Poisson equation. In this paper, an adaptive bandwidth Gaussian kernel density estimator is applied, which reduces the bandwidth where the density is low, and increases the bandwidth where the density is high. Experiments show that such an estimator that makes use of both global and local points distribution can effectively remove noise and outliers in the reconstruction.

Construction of low-cost 3D scanner using triangulation and Screened Poisson Surface Reconstruction techniques

The application of 3D scanner technology among industrial practitioner in Indonesia and Malaysia is still at the beginning stage. Compared to others, this technology has already been adapted mostly by growing country such as China, Korea, US, Germany and Italy. This technology can be seen implemented in manufacturing, aerospace, medical and dentistry industries. The concept of 3D scanner technology is mainly to improve the reverse engineering process. Due to the high cost of 3D scanner machine available in the market, therefore a reasonable cost-effective 3D scanner is needed to be developed especially for education purposes. The objective of this project is to develop low-cost 3D scanning setup to create a mesh of a small-scale object with the help of open-sources software for 3D scanning and 3D mesh processing. Triangulation technique was used for the scanning process to capture the object surface. Screened Poisson Surface Reconstruction techniques was applied to improve the uncomplete and uneven surface mesh. In order to test the setup, 3D scanning was conducted on 4 different objects with different colours and surface finish. The scanning results show that the proposed method produced a good 3D mesh with less noise and less uncomplete surface.

Interactive urban building energy modelling with functional mockup interface of a local residential building stock

The transformation towards a low carbon energy system requires municipalities to improve their local building stock. Urban building energy modelling (UBEM) is an emerging tool to support municipalities in shaping the necessary strategies by estimating energy demand with high spatial and temporal resolution. This study proposes a Functional Mockup Interface (FMI)-based UBEM that enables interactive capacities to simulate diverse environmental conditions without reinitialisation. The FMI-based approach allows to couple the building energy simulation EnergyPlus with external models. These capacities were tested on a real-world example in the German city of Wuppertal with urban microclimate data. The results are estimates of sub-hourly energy demand based on the adjacent environmental conditions of each building. In order to ameliorate the applicability, the FMI-based UBEM is further enriched by incorporating an automatic procedure to derive 3D building models, displaying high geometrical fidelity, from city-wide point clouds through the screened Poisson surface reconstruction algorithm. The study area contains 5736 residential buildings. A diverse residential building stock was modelled on the basis of the EU project TABULA. To demonstrate the functionality of the proposed UBEM, a demand response scenario was constructed with microclimate data and heat pumps instead of other heating and hot water systems. The capacity of UBE-FMI to dynamically change parameters (e.g. thermostat setpoint) in the building models can benefit the evaluation of demand response strategies and its potential to shed peak loads. In comparison with reference studies, UBE-FMI produced reasonable estimates of energy demand. The 3D building models and simulation results using “live” weather are visualized by a web-interface, which is implemented with the geospatial 3D framework NASA WorldWind.

Structural Elements Detection and Reconstruction (SEDR): A Hybrid Approach for Modeling Complex Indoor Structures

We present a hybrid approach for modeling complex interior structural elements from the unstructured point cloud without additional information. The proposed approach focuses on an integrated modeling strategy that can reconstruct structural elements and keep the balance of model completeness and quality. First, a data-driven approach detects the complete structure points of indoor scenarios including the curved wall structures and detailed structures. After applying the down-sampling process to point cloud dataset, ceiling and floor points are detected by RANSAC. The ceiling boundary points are selected as seed points of the growing algorithm to acquire points related to the wall segments. Detailed structures points are detected using the Grid-Slices analysis approach. Second, a model-driven refinement is conducted to the structure points that aims to decrease the impact of point cloud accuracy on the quality of the model. RANSAC algorithm is implemented to detect more accurate layout, and the hole in structure points is repaired in this refinement step. Lastly, the Screened Poisson surface reconstruction approach is conducted to generate the model based on the structure points after refinement. Our approach was validated on the backpack laser dataset, handheld laser dataset, and synthetic dataset, and experimental results demonstrate that our approach can preserve the curved wall structures and detailed structures in the model with high accuracy.

An efficient interpolation‐free algorithm for cylindrical millimeter‐wave holographic three‐dimensional reconstruction

AbstractIn this article, an efficient interpolation‐free algorithm (IFA) applied for cylindrical millimeter‐wave (MMW) holographic three‐dimensional (3‐D) reconstruction is proposed. Compared with previous cylindrical MMW holographic 3‐D reconstruction methods, the proposed IFA can efficiently achieve high‐quality 3‐D reconstruction within the region of interest (ROI). In the IFA, the spherical wave data are decomposed into plane wave components along radial direction and height direction in the wavenumber domain. Then, an angle‐matched filter function containing distance information of the ROI is utilized to reconstruct the corresponding cylindrical coordinate 3‐D imaging result, whose radial maximum projection is then utilized to generate the 3‐D point clouds of target surface. Finally, the 3‐D surface is reconstructed based on the screened Poisson surface reconstruction method. Both simulation and experimental results validate the effectiveness of the proposed method.

Three dimensional stratum interpolation and visualization based on section and borehole data from jointing the moving least square method and poisson reconstruction method

3D geological modeling data can only be measured rarely or indirectly, as it is necessary to interpolate the sparse and uneven geological data in the study area. Given the problems in the interpolation of data from different sources, we have introduced stratum interpolation and visualization technology based on the moving least squares (MLS) method and Poisson surface reconstruction method. The MLS method can not only obtain a smooth stratum surface by adjusting the relevant parameters of its interpolation, but it can also ensure the similarity of the stratum in space and the difference when far part. At the same time, it combines the Poisson reconstruction method and improves it, so that it can reconstruct the open surface by using a virtual outer frame from the original closed figure reconstruction theory. This paper takes the exploration line profile and geological boreholes in Xiushan County, Chongqing as an example, and visualizes them by fusion and the interpolation of data from different sources. The resulting stratum surface is smooth, continuous, accurate, and highly portable.

Three Dimension Scanning using Infrared Coded Light

In this competitive market, reverse engineering is introduced to shorten a new product development time by digitizing an existing product for rapid redesign. In this project, a low cost and standalone 3D scanner is being developed to achieve this purpose. Research is done comparing advantages and limitation of different hardware and method for 3D scanning. Scanner is assisted by automated rotating and illumination scanning platform to improve its performance. Software is developed for scanning of object using a Red-Green-Blue-Depth (RGBD) camera. The scanned result is processed and refined using global and local registration method. Poisson surface reconstruction is performed on the processed point clouds for generating triangular mesh. This scanner able to scan the object with the accuracy up to 5mm with computational time around 25 minutes.

Poisson Surface Reconstruction with Envelope Constraints

AbstractReconstructing surfaces from scanned 3D points has been an important research area for several decades. One common approach that has proven efficient and robust to noise is implicit surface reconstruction, i.e. fitting to the points a 3D scalar function (such as an indicator function or signed‐distance field) and then extracting an isosurface. Though many techniques fall within this category, existing methods either impose no boundary constraints or impose Dirichlet/Neumann conditions on the surface of a bounding box containing the scanned data.In this work, we demonstrate the benefit of supporting Dirichlet constraints on a general boundary. To this end, we adapt the Screened Poisson Reconstruction algorithm to input a constraint envelope in addition to the oriented point cloud. We impose Dirichlet boundary conditions, forcing the reconstructed implicit function to be zero outside this constraint surface. Using a visual hull and/or depth hull derived from RGB‐D scans to define the constraint envelope, we obtain substantially improved surface reconstructions in regions of missing data.