Surface Reconstruction Method Research Articles

Learning neural implicit surfaces with local probability standard variance

AbstractReconstructing geometric shapes from sparse multiview has always been a challenging task. With the development of neural implicit surfaces, geometry‐based volume rendering surface reconstruction methods have been proven to be able to reconstruct high‐quality surfaces. However, existing geometry‐based reconstruction methods completely associate volume density with signed distance function or unsigned distance function, resulting in the same volume density peak that can only be reconstructed near the object surface. When there are transparent surfaces in the scene, existing methods prioritize the reconstruction of opaque surfaces, neglecting the reconstruction of transparent surfaces, which is disadvantageous when reconstructing real scenes. To solve this problem, we introduce local probability standard variance, which calculates volume density together with signed distance function. In this way, it can reconstruct the volume density that matches the transparency characteristics of the object surface. The method can reconstruct the surface of transparent objects, and experiments on two transparent surface datasets show that the method performs better.

Surface reconstruction of Fe doping NiC2O4 by the electrocatalytic reaction to improve the performance of overall water splitting

The most environmentally friendly method for producing hydrogen is through the electrolysis of water, reliable and effective catalysts play a crucial role in enhancing hydrogen production efficiency. In this paper, Fe–NiC2O4/NF in block form was prepared by hydrothermal method on nickel foam (NF) for electrolysis of water under alkaline conditions. The surface reconstruction of the Fe–NiC2O4/NF was further investigated via XRD, FTIR, and XPS during the oxygen evolution reaction (OER). The catalytic activity of Fe–Ni(OH)2/NiOOH formed after reconstruction was considerably enhanced in comparison to the original catalyst. The overpotentials for hydrogen evolution reaction (HER) at 10 mA cm−2 and OER at 100 mA cm−2 were 126 mV and 260 mV, respectively. Meanwhile, applying it in a two-electrode, total hydrolysis device only requires 1.53 V at 10 mA cm−2. This investigation introduces a novel method for surface reconstruction of electrocatalysts to facilitate electrolytic water reaction.

Multiphase Reconstruction of Heterogeneous Materials Using Machine Learning and Quality of Connection Function.

Establishing accurate structure-property linkages and precise phase volume accuracy in 3D microstructure reconstruction of materials remains challenging, particularly with limited samples. This paper presents an optimized method for reconstructing 3D microstructures of various materials, including isotropic and anisotropic types with two and three phases, using convolutional occupancy networks and point clouds from inner layers of the microstructure. The method emphasizes precise phase representation and compatibility with point cloud data. A stage within the Quality of Connection Function (QCF) repetition loop optimizes the weights of the convolutional occupancy networks model to minimize error between the microstructure's statistical properties and the reconstructive model. This model successfully reconstructs 3D representations from initial 2D serial images. Comparisons with screened Poisson surface reconstruction and local implicit grid methods demonstrate the model's efficacy. The developed model proves suitable for high-quality 3D microstructure reconstruction, aiding in structure-property linkages and finite element analysis.

Three-dimensional surface reconstruction from reflectance direction fields with orthogonal multicolor filters

A three-dimensional (3D) freeform surface reconstruction method is proposed, specifically designed to handle complex surfaces, even those with steep inclinations. This method utilizes a one-shot color mapping imaging system to obtain reflectance direction fields using a stripe multicolor filter positioned in two orthogonal orientations. This imaging system can capture reflectance directions that are identifiable by corresponding colors. The multicolor filter consists of multiple stripe regions, each with a different transmission spectrum. Theoretically, an equation that describes the relationship between reflectance direction fields and the reflective 3D surface can be derived based on geometrical optics. By solving this equation using a deep neural network (DNN) as a gradient descent method without any training data, the 3D surface can be accurately reconstructed, even for surfaces with steep inclinations. The effectiveness of this method is validated through numerical demonstrations on several 3D surfaces, including complex ones with concave and convex areas, as well as steep inclinations.

Asteroid-NeRF: A deep-learning method for 3D surface reconstruction of asteroids

Asteroids preserve important information about the origin and evolution of the Solar System. Three-dimensional (3D) surface models of asteroids are essential for exploration missions and scientific research. Regular methods for 3D surface reconstruction of asteroids, such as stereo-photogrammetry (SPG), usually struggle to reconstruct textureless areas and can only generate sparse surface models. Stereo-photoclinometry (SPC) can reconstruct pixel-wise topographic details but its performance depends on the availability of images obtained under different illumination conditions and suffers from uncertainties related to surface reflectance and albedo. This paper presents Asteroid-NeRF, a novel deep-learning method for 3D surface reconstruction of asteroids that is based on the state-of-the-art neural radiance field (NeRF) method. Asteroid-NeRF uses a signed distance field (SDF) to reconstruct a 3D surface model from multi-view posed images of an asteroid. In addition, Asteroid-NeRF incorporates appearance embedding to adapt to different illumination conditions and to maintain the geometric consistency of a reconstructed surface, allowing it to deal with the different solar angles and exposure conditions commonly seen in asteroid images. Moreover, Asteroid-NeRF incorporates multi-view photometric consistency to constrain the SDF, enabling optimised reconstruction. Experimental evaluations using actual images of asteroids Itokawa and Bennu demonstrate the promising performance of Asteroid-NeRF, complementing traditional methods such as SPG and SPC. Furthermore, due to the global consistency and pixel-wise training of Asteroid-NeRF, it produces highly detailed surface reconstructions. Asteroid-NeRF offers a new and effective solution for high-resolution 3D surface reconstruction of asteroids that will aid future exploratory missions and scientific research on asteroids.

Efficient Particle-Based Fluid Surface Reconstruction Using Mesh Shaders and Bidirectional Two-Level Grids

In this paper, we introduce a novel method for particle-based fluid surface reconstruction that incorporates mesh shaders for the first time. This approach eliminates the need to store triangle meshes in a GPU's global memory, resulting in a significant reduction in the amount of memory needed. Furthermore, our method employs a bidirectional two-level uniform grid, which not only accelerates the computationally expensive stage of surface cell detection but also effectively addresses the issue of vertex overflow among the mesh shaders. Experimental results prove that our method outperforms the state-of-the-art method, achieving both acceleration and memory reductions simultaneously, without sacrificing quality. The method is highly practical, with no major limitations other than requiring a GPU that supports the mesh shaders.

Flipping-based iterative surface reconstruction for unoriented points

In this paper, we propose a novel surface reconstruction method for unoriented points by establishing and solving a nonlinear equation system. By treating normals as unknown parameters and imposing the conditions that the implicit field is constant and its gradients parallel to the normals on the input point cloud, we establish a nonlinear equation system involving the oriented normals. To simplify the system, we transform it into a 0-1 integer programming problem solely focusing on orientation by incorporating inconsistent oriented normal information through PCA. We solve the simplified problem using flipping-based iterative algorithms and propose two novel criteria for flipping based on theoretical analysis.Extensive experiments on renowned datasets demonstrate that our flipping-based method with wavelet surface reconstruction achieves state-of-the-art results in orientation and reconstruction. Furthermore, it exhibits linear computational and storage complexity by leveraging the orthogonality and compact support properties of wavelet bases. The source code is available at https://github.com/mayueji/FISR_code.

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.

Fabrication of electrical devices on conformal surfaces based on array nozzle and five-axis motion mechanism

It has been known that conformal printing with a single nozzle has low efficiency. This study proposes a novel non-expandable curved surface printing method that could realize high-efficiency printing with array nozzles. First, based on the non-uniform rational B-splines curved surface reconstruction method, the parameter equation of the stereolithography format curved surface model is obtained. An equidistant parallel curve filling method is proposed based on the parameter equation. According to the filling accuracy, a spatial equidistant curve network model is established by fitting the surface. Using each node on the curve network as the filling pixel increases the filling rate of the non-expandable surface and improves the printing accuracy. Compared with that of the traditional equidistant filling, the droplet filling rate could be increased by 13.05 % in this study. Finally, based on the self-developed five-axis curved surface inkjet printing system and normal vector printing method, samples of concave, convex, and multi-material conformal surfaces are sequentially printed. Our research finding lays the foundation for high-efficiency integrated molding of electronic devices with complex conformal surfaces.

Preprocessing method for robust topography reconstruction of surfaces of metal additive manufactured parts based on focus variation microscopy

Abstract When using an areal measuring optical instrument to measure rough surfaces, especially surfaces generated by metal additive manufacturing (e.g. laser and electron beam powder bed fusion), topographical artifacts such as spikes on a reconstructed surface are nearly unavoidable. These artifacts may affect the determination of surface roughness parameters and lead to erroneous surface features. This paper proposes a new preprocessing method to eliminate most artifacts before extracting surface heights of rough surfaces measured by focus variation microscopy. In this method, the axial region where a surface height value is located with the highest probability is estimated, based on datasets of planes parallel to the axial scanning direction. Results regarding height measurements with and without the preprocessing method are compared by measuring a Rubert Microsurf 329 comparator test panel for reference and workpieces produced by metal additive manufacturing.

INTRAOPERATIVE SURGICAL NAVIGATION BASED ON LASER SCANNER FOR IMAGE-GUIDED ORAL AND MAXILLOFACIAL SURGERY

In oral and maxillofacial surgery, computer-assisted navigation technologies have been widely used to achieve intraoperative positioning. The traditional methods mainly rely on the experience of doctors and the difference between the locations of key points in the surgical area and the preoperative planning, which have certain limitations. In this paper, a new intraoperative surgical navigation framework based on mobile laser scanner is proposed, which ensures that surgery is performed accurately according to the preoperative planning. The framework mainly includes two parts. First, the real-time surface reconstruction of the anatomy should be realized during the operation. Second, the acquired image is matched to the planned image in real time. Although the most common method of surface reconstruction is to render the volume directly from raw data or render the surface from the segmented data using computed tomography/magnetic resonance (CT/MR) data, this method is too complicated for performing the real-time operation during surgery. Furthermore, a new surface registration technique is proposed for image-guided oral and maxillofacial surgery based on the point sets. To improve the registration accuracy and robustness, the point sets are modeled by Mixed Student’s t-Distribution model. In the experiments, the point sets of CT data are from 10 patients with craniomaxillofacial diseases and the surface point set is from the LRS. The TRE of 10 data was less than 1[Formula: see text]mm. Compared with the paired-point registration method and Iterative Closest Point algorithm, the results demonstrated better performance of the proposed method, the surgical situation can be displayed in real time during the surgical process, and any differences from the surgical plan can also be reflected.

Agent-Based Principal Strips Modeling for Freeform Surfaces in Architecture

The principal curvature (PC) of a freeform surface, as an important indicator of its fundamental features, is frequently used to guide their rationalization in the field of architectural geometry. The division of a surface using its PC lines into principal strips (PSs) is an innovative way to break down a freeform surface for construction. However, the application of PC networks in architectural design is hindered by the difficulty to generate them and flexibly control their density. This paper introduces a method for PS-based reconstruction of freeform surfaces with different umbilical conditions in the early stages of design. An agent-based modeling approach is developed to find the umbilics and increase the degree of control over the spacing of PC lines. This research can effectively expand the application range of PS-based surface reconstruction methods for freeform architectures.

RECONSTRUCTION OF THE CORNEAL SURFACE OF THE HUMAN EYE USING A COMPUTATIONAL EVOLUTIONARY ALGORITHM. PRACTICAL APPLICATION IN NON-PATHOLOGICAL CASES

Increasingly, the use of geometric modelling techniques in Applied Ophthalmology is significant in the characterization of important pathologies of the cornea, such as Keratoconus. This article presents a novel method for the geometric reconstruction of the corneal surface from optical topography using a genetic algorithm. Traditionally, mathematical programming methods such as the least squares method have been used to obtain the coefficients of the corneal surface function, such as Navarro model or Zernike polynomials. This new method uses non-dominated multivariable genetic algorithm optimization to obtain the surface function coefficients from the point cloud obtained with corneal topographer device. Once the reconstruction is performed, the surface is represented using CAD software, and morphogeometric parameters are obtained. The experimental sample consisted in 33 healthy patients eyes, aged from 11 to 63, and without previous ocular surgeries or pathologies. Topographic data were obtained using a Scheimpflug Sirius tomographer (CSO, Italy). The computational optimization was executed under Matlab software environment (Mathworks, USA). The new method provides a lower mean squared error (MSE) than those obtained by the least squares or the nonlinear programming algorithms. Thus, the morphogeometric parameters obtained from the patient's corneas fit better, allowing for a better analysis of real clinical conditions.

Surface Reconstruction and Error Compensation Method Based on NSGA-II Sensing Position Optimization

Surface Reconstruction and Error Compensation Method Based on NSGA-II Sensing Position Optimization

Surface Reconstruction from Point Clouds: A Survey and a Benchmark.

Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete point cloud observation is a long-standing problem in computer vision and graphics research. The problem is technically ill-posed, and becomes more difficult considering that various sensing imperfections would appear in the point clouds obtained by practical depth scanning. In literature, a rich set of methods has been proposed, and reviews of existing methods are also provided. However, existing reviews are short of thorough investigations on a common benchmark. The present paper aims to review and benchmark existing methods in the new era of deep learning surface reconstruction. To this end, we contribute a large-scale benchmarking dataset consisting of both synthetic and real-scanned data; the benchmark includes object- and scene-level surfaces and takes into account various sensing imperfections that are commonly encountered in practical depth scanning. We conduct thorough empirical studies by comparing existing methods on the constructed benchmark, and pay special attention on robustness of existing methods against various scanning imperfections; we also study how different methods generalize in terms of reconstructing complex surface shapes. Our studies help identity the best conditions under which different methods work, and suggest some empirical findings. For example, while deep learning methods are increasingly popular in the research community, our systematic studies suggest that, surprisingly, a few classical methods perform even better in terms of both robustness and generalization; our studies also suggest that the practical challenges of misalignment of point sets from multi-view scanning, missing of surface points, and point outliers remain unsolved by all the existing surface reconstruction methods. We expect that the benchmark and our studies would be valuable both for practitioners and as a guidance for new innovations in future research. We make the benchmark publicly accessible at https://Gorilla-Lab-SCUT.github.io/SurfaceReconstructionBenchmark.

Scalable enforcement of geometric non-interference constraints for gradient-based optimization

AbstractMany design optimization problems include constraints to prevent intersection of the geometric shape being optimized with other objects or with domain boundaries. When applying gradient-based optimization to such problems, the constraint function must provide an accurate representation of the domain boundary and be smooth, amenable to numerical differentiation, and fast-to-evaluate for a large number of points. We propose the use of tensor-product B-splines to construct an efficient-to-evaluate level set function that locally approximates the signed distance function for representing geometric non-interference constraints. Adapting ideas from the surface reconstruction methods, we formulate an energy minimization problem to compute the B-spline control points that define the level set function given an oriented point cloud sampled over a geometric shape. Unlike previous explicit non-interference constraint formulations, our method requires an initial setup operation, but results in a more efficient-to-evaluate and scalable representation of geometric non-interference constraints. This paper presents the results of accuracy and scaling studies performed on our formulation. We demonstrate our method by solving a medical robot design optimization problem with non-interference constraints. We achieve constraint evaluation times on the order of $$10^{-6}$$ 10 - 6 seconds per point on a modern desktop workstation, and a maximum on-surface error of less than 1.0% of the minimum bounding box diagonal for all examples studied. Overall, our method provides an effective formulation for non-interference constraint enforcement with high computational efficiency for gradient-based design optimization problems whose solutions require at least hundreds of evaluations of constraints and their derivatives.

A novel three-dimensional surface reconstruction method for fabric appearance evaluation

Traditional fabric appearance evaluation method mainly relies on subjective and manual operation, which is not uniform and time-consuming. In this paper, one set of self-developed 3D reconstruction system and method based on photometric stereo is proposed for objective fabric appearance evaluation. A photometric stereo image acquisition system equipped with multi-directional lighting sources is developed for the 3D surface reconstruction. Lighting and camera calibration is conducted to obtain the position of light sources and the intrisic parameters of digital camera. Our experimental results show that 3D fabric surface could be reconstructed effectively based on photometric stereo vision method, it has the potential to be used for fabric appearance evaluation.

Full-aperture surface reconstruction method for shift-rotation absolute measurement with tilt error suppression

We propose a new surface reconstruction method for the absolute measurement of shift rotation that can obtain a full-aperture surface distribution. Our approach reduces the effect of tilt errors on surface reconstruction by increasing the translation ratio of the test mirror. This method requires the test mirror to be measured once in situ, after translation, and after rotation. Because the known measured wavefront is a nonlinear function of the absolute surface distribution, the function of the surface distribution is linearized by performing a Taylor expansion to achieve fast surface reconstruction. Finally, a linear system of equations is constructed to solve the absolute surface distribution. The coefficient matrix is sparse and can quickly and accurately reconstruct the absolute surface distribution. In the simulation, the root mean square error (RMSE) of the reconstructed surface distribution of the method discussed in this study can reach to 0.012nm. Moreover, our method can significantly reduce the effect of tilt errors compared with typical methods, thereby yielding a new, high-precision solution for absolute measurements in interferometry.

SSRNet: Scalable 3D Surface Reconstruction Network.

Learning-based surface reconstruction methods have received considerable attention in recent years due to their excellent expressiveness. However, existing learning-based methods lack scalability in processing large-scale point clouds. This paper proposes a novel scalable learning-based 3D surface reconstruction method based on octree, called SSRNet. SSRNet works in a scalable reconstruction pipeline, which divides oriented point clouds into different local parts and then processes them in parallel. Accommodating this scalable design pattern, SSRNet constructs local geometric features for octree vertices. Such features comprise the relation between the vertices and the implicit surface, ensuring geometric perception. Focusing on local geometric information also enables the network to avoid the overfitting problem and generalize well on different datasets. Finally, as a learning-based method, SSRNet can process large-scale point clouds in a short time. And to further solve the efficiency problem, we provide a lightweight and efficient version that is about five times faster while maintaining reconstruction performance. Experiments show that our methods achieve state-of-the-art performance with outstanding efficiency.

Learning joint surface reconstruction and segmentation, from brain images to cortical surface parcellation.

Reconstructing and segmenting cortical surfaces from MRI is essential to a wide range of brain analyses. However, most approaches follow a multi-step slow process, such as a sequential spherical inflation and registration, which requires considerable computation times. To overcome the limitations arising from these multi-steps, we propose SegRecon, an integrated end-to-end deep learning method to jointly reconstruct and segment cortical surfaces directly from an MRI volume in one single step. We train a volume-based neural network to predict, for each voxel, the signed distances to multiple nested surfaces and their corresponding spherical representation in atlas space. This is, for instance, useful for jointly reconstructing and segmenting the white-to-gray-matter interface and the gray-matter-to-CSF (pial) surface. We evaluate the performance of our surface reconstruction and segmentation method with a comprehensive set of experiments on the MindBoggle, ABIDE and OASIS datasets. Our reconstruction error is found to be less than 0.52 mm and 0.97 mm in terms of average Hausdorff distance to the FreeSurfer generated surfaces. Likewise, the parcellation results show over 4% improvements in average Dice with respect to FreeSurfer, in addition to an observed drastic speed-up from hours to seconds of computation on a standard desktop station.