Fine Geometric Details Research Articles

NeRF-Texture: Synthesizing Neural Radiance Field Textures.

Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. In addition to generating NeRF textures over a planar domain, our method can also synthesize NeRF textures over curved surfaces, which are practically useful. Experimental results and evaluations demonstrate the effectiveness of our approach.

Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis

While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensive methods that model the scene's geometry as a volumetric density field (e.g. NeRF) excel at reconstructing fine geometric detail. However, density fields often represent geometry in a "fuzzy" manner, which hinders exact localization of the surface. In this work, we modify density fields to encourage them to converge towards surfaces, without compromising their ability to reconstruct thin structures. First, we employ a discrete opacity grid representation instead of a continuous density field, which allows opacity values to discontinuously transition from zero to one at the surface. Second, we anti-alias by casting multiple rays per pixel, which allows occlusion boundaries and subpixel structures to be modelled without using semi-transparent voxels. Third, we minimize the binary entropy of the opacity values, which facilitates the extraction of surface geometry by encouraging opacity values to binarize towards the end of training. Lastly, we develop a fusion-based meshing strategy followed by mesh simplification and appearance model fitting. The compact meshes produced by our model can be rendered in real-time on mobile devices and achieve significantly higher view synthesis quality compared to existing mesh-based approaches. Our interactive webdemo is available at https://binary-opacity-grid.github.io.

Sparse convolutional neural network for high-resolution skull shape completion and shape super-resolution

Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of high-resolution shapes represented as binary voxel occupancy grids. In contrast to a dense CNN that takes the entire voxel grid as input, a sparse CNN processes only on the non-empty voxels, thus reducing the memory and computation overhead caused by the sparse input data. We evaluate our method on two clinically relevant skull reconstruction tasks: (1) given a defective skull, reconstruct the complete skull (i.e., skull shape completion), and (2) given a coarse skull, reconstruct a high-resolution skull with fine geometric details (shape super-resolution). Our method outperforms its dense CNN-based counterparts in the skull reconstruction task quantitatively and qualitatively, while requiring substantially less memory for training and inference. We observed that, on the 3D skull data, the overall memory consumption of the sparse CNN grows approximately linearly during inference with respect to the image resolutions. During training, the memory usage remains clearly below increases in image resolution—an times 8 increase in voxel number leads to less than times 4 increase in memory requirements. Our study demonstrates the effectiveness of using a sparse CNN for skull reconstruction tasks, and our findings can be applied to other spatially sparse problems. We prove this by additional experimental results on other sparse medical datasets, like the aorta and the heart. Project page at https://github.com/Jianningli/SparseCNN.

Fine Back Surfaces Oriented Human Reconstruction for Single RGB‐D Images

AbstractCurrent single RGB‐D image based human surface reconstruction methods generally take both the RGB images and the captured frontal depth maps together so that the 3D cues from the frontal surfaces can help infer the full surface geometries. However, we observe that the back surfaces can often be quite different from the frontal surfaces and, therefore, current methods can mess the recovery process by adopting such 3D cues, especially for the unseen back surfaces. We need to do the back surface inference without the frontal depth map. Consequently, a novel human reconstruction framework is proposed, so that human models with fine geometric details, especially for the back surfaces, can be obtained. In this approach, a progressive estimation method is introduced to effectively recover the unseen back depth maps. The coarse back depth maps are recovered by the parametric models of the subjects, with the fine ones further obtained by the normal‐maps conditioned GAN. This framework also includes a cross‐attention based denoising method for the frontal depth maps. This method adopts the cross attention between the features of the last two layers encoded from the frontal depth maps and thus suppresses the noise for fine depth maps by the attentions of features from the low‐noise and globally‐structured highest layer. Experimental results show the efficacies of the proposed ideas.

A One-Step Leapfrog ADI Procedure with Improved Absorption for Fine Geometric Details

Based on the alternating direction implicit (ADI) procedure, leapfrog formulation, and a higher order PML scheme, we propose an unconditionally stable perfectly matched layer (PML) algorithm with improved absorption to treat open regions in a finite computational domain with improved overall performance. The proposed algorithm performed well compared to other algorithms during simulations. We further demonstrated the proposed scheme’s effectiveness using numerical examples. We found that the proposed scheme had enhanced effectiveness and improved the absorption during the whole simulation. Furthermore, it was able to break the stability condition, proving that it is an unconditionally stable scheme.

Antenna In-Situ Performance Analysis for the Hypersonic Flight Vehicle HEXAFLY: Employing measurement data in a simulation model

An antenna in-situ performance analysis for the hypersonic flight vehicle HEXAFLY is presented. Two simulation tools and a compact antenna test range (CATR) measurement facility are employed to obtain the embedded radiation characteristics of two different antenna systems. In a first step, both antenna types are simulated and measured without the impact of the flight vehicle, but in an environment supporting correct functioning, e.g., on a finite conducting ground plane or on top of a vertical conducting stabilizer. A finite-element method (FEM) solver is used for this detailed full-wave analysis of antenna structures with fine geometrical and material details. Next, the FEM solver and a method of moments (MoM) solver with multilevel fast multipole algorithm (MLFMA) acceleration are used to obtain the in-situ radiation characteristic of both antennas mounted on the flight vehicle. The FEM solver is now utilized to simulate the complete setup, whereas the MoM solver works with an equivalent radiation source, which is obtained from CATR measurements of isolated antennas. The results confirm the effectiveness of the measurement data-based two-stepapproach, which helps to overcome the limitations of pure simulations that are often not feasible for off-the-shelf antennas and allows the overall validation process to be viewed with added confidence.

Dermoscopic skin lesion image segmentation based on Local Binary Pattern Clustering: Comparative study

Accurate skin lesion segmentation is important for identification and classification through computational methods. However, when performed by dermatologists, the results of clinical segmentation are affected by a certain margin of inaccuracy (which exists since dermatologist do not delineate lesions for segmentation but for extraction) and also significant inter- and intra-individual variability, such segmentation is not sufficiently accurate for segmentation studies. This work addresses these limitations to enable detailed analysis of lesions’ geometry along with extraction of non-linear characteristics of region-of-interest border lines. A comprehensive review of 39 segmentation methods is carried out and a contribution to improve dermoscopic image segmentation is presented to determine the regions-of-interest of skin lesions, through accurate border lines with fine geometric details. This approach resorts to Local Binary Patterns and k-means clustering for precise identification of lesions boundaries, particularly the melanocytic. A comparative evaluation study is carried out using three different datasets and reviewed algorithms are grouped according to their approach. Results show that algorithms from the same group tend to perform similarly. Nevertheless, their performance does not depend uniquely on the algorithm itself but also on the underlying dataset characteristics. Throughout several evaluations, the proposed Local Binary Patterns method presents, consistently, better average performance than the current state-of-the-art techniques across the three different datasets without the need of training or supervised learning steps. Overall, apart from presenting a new segmentation method capable of outperforming the current state-of-the-art, this paper provides insightful information about the behaviour and performance of different image segmentation algorithms.

The Use of Photogrammetric Techniques in Documenting Cultural Heritage: The Example of Aksaray Selime Sultan Tomb

Forming 3 dimensional models of the existing historical buildings and objects has a great importance in terms of conserving historical works, sustaining our history, and being able to transfer them to the next generations. For catching the fine geometrical views and details and digitally modelling, it is important to identify a suitable method. It is necessary to digitally record, document, and conserve our cultural assets due to still ongoing wars, natural disasters, and climatic changes. The simplest way for permanently storing the original form and location of historical works with high quality views and details in digital media, forming base for the different projects, and making ready for use, if necessary, is to model the works in 3 dimensions. Beginning from the increase of importance to historical works, in parallelism with this, the importance and sorts of documentation also have begun to increase. Among the existing documentation methods, the method that is suitable to work in metric (via measurement), visual (photographing) and digital (computerized) media and appropriate in terms of time, space, and cost is photogrammetry. In forming and documenting 3 dimensional model of Selime Sultan Tomb located near Ihlara Valley, two different kinds of software of photogrammetric assessment were used.

Detail-preserved real-time hand motion regression from depth

This paper aims to address the very challenging problem of efficient and accurate hand tracking from depth sequences, meanwhile to deform a high-resolution 3D hand model with geometric details. We propose an integrated regression framework to infer articulated hand pose, and regress high-frequency details from sparse high-resolution 3D hand model examples. Specifically, our proposed method mainly consists of four components: skeleton embedding, hand joint regression, skeleton alignment, and high-resolution details integration. Skeleton embedding is optimized via a wrinkle-based skeleton refinement method for faithful hand models with fine geometric details. Hand joint regression is based on a deep convolutional network, from which 3D hand joint locations are predicted from a single depth map, then a skeleton alignment stage is performed to recover fully articulated hand poses. Deformable fine-scale details are estimated from a nonlinear mapping between the hand joints and per-vertex displacements. Experiments on two challenging datasets show that our proposed approach can achieve accurate, robust, and real-time hand tracking, while preserve most high-frequency details when deforming a virtual hand.

Robust Multiview Photometric Stereo Using Planar Mesh Parameterization.

We propose a robust uncalibrated multiview photometric stereo method for high quality 3D shape reconstruction. In our method, a coarse initial 3D mesh obtained using a multiview stereo method is projected onto a 2D planar domain using a planar mesh parameterization technique. We describe methods for surface normal estimation that work in the parameterized 2D space that jointly incorporates all geometric and photometric cues from multiple viewpoints. Using an estimated surface normal map, a refined 3D mesh is then recovered by computing an optimal displacement map in the same 2D planar domain. Our method avoids the need of merging view-dependent surface normal maps that is often required in conventional methods. We conduct evaluation on various real-world objects containing surfaces with specular reflections, multiple albedos, and complex topologies in both controlled and uncontrolled settings and demonstrate that accurate 3D meshes with fine geometric details can be recovered by our method.

A boundary element method for eddy-current problems in fusion devices

In magnetic confinement fusion devices, close-fitting passive conducting structures surrounding the plasma are an efficient way to slow down the growth rate of magnetohydrodynamic (MHD) instabilities, enabling the possibility of an active stabilization by means of external actuators (coils). Nevertheless, a fully quantitative investigation, under realistic 3D boundary conditions, is still a very hard challenge. Moreover, the inclusion of fine geometrical details of actual passive structures might easily lead to impractical memory and computational time requirements if the problem is not carefully addressed. In this work we present an effective technique to solve eddy current problems in thin conductors of arbitrary topology by a boundary element method (BEM) based on a stream function. The proposed technique is applied to study the dynamic response of the stabilizing shell of RFX-mod, both to axisymmetric and non-axisymmetric external fields.

The Ryanodine Receptor Patchwork: Knitting Calcium Spark Dynamics

The Ryanodine Receptor Patchwork: Knitting Calcium Spark Dynamics

Refinement of the Thermal Press Curing Process for Advanced Composites

Thermal press curing (TPC) is an alternative process to autoclaving for consolidating and curing thermoset and thermoplastic prepreg composite parts by pressing them between a heated “curing mold” and a customized rubber-faced “base mold” that are engineered to provide uniform temperature and pressure conditions. A study was performed with a kayak paddle part made from eight plies of woven carbon/epoxy prepreg material and formed by double diaphragm forming (DDF). The study expounds on the narrow body of TPC knowledge around three main objectives: (1) to experimentally compare TPC cured parts to a benchmark autoclave process using a realistic part shape with fine geometrical details, (2) to evaluate the necessity of vacuum bagging of TPC cured parts, and (3) to characterize the robustness/sensitivities of pressure application during the TPC process by varying both the total pressure applied to the base mold and the location the hydraulic press ram applied pressure to the base mold. Maximum temperature and pressure variations around the target levels over the entire clamped tool surface were measured as 5.0 °C and 5.5%, respectively, both of which were well within the manufacturer's recommendations. The TPC part had fewer defects, was generally thinner, and had a higher fiber volume fraction than a comparable autoclaved part. Little difference was observed between the TPC parts made with and without vacuum bagging. Parts with too little pressure (90%) resulted in more thickness variation and defects than too much pressure (110%). Finally, TPC parts exhibit some thickness variation, as expected, when ram force is applied off the center of pressure (COP).

Adaptive Mesh Refinement and Superconvergence for Two-Dimensional Interface Problems

Adaptive mesh refinement and the Borgers algorithm are combined to generate a body-fitted mesh which can resolve the interface with fine geometric details. Standard linear finite element method bas...

Extended State-Space Finite-Element Formulation to Incorporate Frequency Dependent Surface Models in Time Domain

This study proposes a finite-element formulation that can incorporate the frequency dispersive effects of surfaces composed of fine geometric details without the necessity of generating a refined mesh to capture these details. The finite-element formulation is recast as a state-space model in time domain and the surface is replaced by its own macroscopic state-space model being part of the whole system of equations to be solved. Examples of metallic surfaces containing small circular holes and slots have been successfully implemented. Simulations of these structures have shown memory savings to one tenth and CPU time reduction to one percent.

Scalable real-time volumetric surface reconstruction

We address the fundamental challenge of scalability for real-time volumetric surface reconstruction methods. We design a memory efficient, hierarchical data structure for commodity graphics hardware, which supports live reconstruction of large-scale scenes with fine geometric details. Our sparse data structure fuses overlapping depth maps from a moving depth camera into a single volumetric representation, from which detailed surface models are extracted. Our hierarchy losslessly streams data bidirectionally between GPU and host, allowing for unbounded reconstructions. Our pipeline, comprised of depth map post-processing, camera pose estimation, volumetric fusion, surface extraction, and streaming, runs entirely in real-time. We experimentally demonstrate that a shallow hierarchy with relatively large branching factors yields the best memory/speed tradeoff, consuming an order of magnitude less memory than a regular grid. We compare an implementation of our data structure to existing methods and demonstrate higher-quality reconstructions on a variety of large-scale scenes, all captured in real-time.

USING TERRESTRIAL LASER SCANNING AND LIDAR DATA FOR PHOTO-REALISTIC VISUALISATION OF CLIMATE IMPACTS AT HERITAGE SITES

Abstract. Remote sensing technologies such as terrestrial laser scanning (TLS) and light detection and ranging (LIDAR) can now provide accurate spatial data for describing topographic patterns in landscapes and mapping the fine geometric detail of complex structures. Interdisciplinary studies using such data for characterising heritage sites are now widespread, but it is less common for data derived from these technologies to be used in an operational setting to 'engage' local people with the idea of future change. Three- dimensional landscape models using TLS and LIDAR are a powerful way of communicating climate change to lay audiences on a local level and the use of multi-scale spatial data can help to show the anticipated impacts of climate change at heritage sites. The aim of this paper is to present the results of a project designed to produce a photorealistic geospatial model of the historic quayside at Cotehele Quay, Cornwall, UK. Our central aim was to generate a spatially accurate and visually realistic three-dimensional model of the site for use in local engagement strategies. TLS and LIDAR data were collected and processed to produce a three-dimensional model. The key image processing stages included: registration to a national co-ordinate system, meshing using 3D reshaper and visualisation creation in 3DS Max. The resulting model will permit spatial consideration of the impact of management strategies at the site. The over-arching application of this method beyond the study site will provide a platform for discussion that addresses the needs of site managers and the expectations of local communities.

Template-Based 3D Model Fitting Using Dual-Domain Relaxation

We introduce a template fitting method for 3D surface meshes. A given template mesh is deformed to closely approximate the input 3D geometry. The connectivity of the deformed template model is automatically adjusted to facilitate the geometric fitting and to ascertain high quality of the mesh elements. The template fitting process utilizes a specially tailored Laplacian processing framework, where in the first, coarse fitting stage we approximate the input geometry with a linearized biharmonic surface (a variant of LS-mesh), and then the fine geometric detail is fitted further using iterative Laplacian editing with reliable correspondence constraints and a local surface flattening mechanism to avoid foldovers. The latter step is performed in the dual mesh domain, which is shown to encourage near-equilateral mesh elements and significantly reduces the occurrence of triangle foldovers, a well-known problem in mesh fitting. To experimentally evaluate our approach, we compare our method with relevant state-of-the-art techniques and confirm significant improvements of results. In addition, we demonstrate the usefulness of our approach to the application of consistent surface parameterization (also known as cross-parameterization).

A Simplified Pump Tower Approach for Realistic CFD Simulation of Sloshing in LNG Tanks

The complexity of the sloshing analysis for liquefied natural gas carriers can be reduced by neglecting the pump tower. The validity of this assumption is examined by studying the effect of the pump tower, located near the aft bulkhead of a typical LNG tank on the sloshing flow evolution. Results are compared for surge-induced sloshing in a rectangular tank with pump tower to that without such an obstruction. A commercial flow solver is used to solve the unsteady Reynolds Averaged Navier Stokes equations for an inhomogeneous multiphase flow. Initial validation of the sloshing flow uses the experimental data of Hinatsu. It was found that a simplified pump tower consisting of a single vertical tube was suitable to capture the effect of the pump tower without the necessity of discretising the fine geometric detail of the pump tower structure. A suitable size for the simplified tower diameter was found using the total fluid force on a real pump tower in a steady flow for a similar range of Reynolds Number. This reduces the required mesh size by an order of magnitude. Although it is found that the effect of the pump tower on the overall force levels is small reductions of local impact pressures of up to 50% are observed and the sloshing flow develops a phase lag compared to the unobstructed tank.

Turning images into 3-D models

In this article developments and performance analysis of image matching for detailed surface reconstruction of heritage objects is discussed. Three dimensional image-based modeling of heritages is a very interesting topic with many possible applications. In this article we propose a multistage image-based modeling approach that requires only a limited amount of human interactivity and is capable of capturing the fine geometric details with similar accuracy as close-range active range sensors. It can also cope with wide baselines using several advancements over standard stereo matching techniques. Our approach is sequential, starting from a sparse basic segmented model created with a small number of interactively measured points. This model, specifically the equation of each surface, is then used as a guide to automatically add the fine details. The following three techniques are used, each where best suited, to retrieve the details: 1) for regularly shaped patches such as planes, cylinders, or quadrics, we apply a fast relative stereo matching technique. 2) For more complex or irregular segments with unknown shape, we use a global multi-image geometrically constrained technique. 3) For segments unsuited for stereo matching, we employ depth from shading (DFS). The goal is not the development of a fully automated procedure for 3D object reconstruction from image data or a sparse stereo approach, but we aim at the digital reconstruction of detailed and accurate surfaces from calibrated and oriented images for practical daily documentation and digital conservation of wide variety of heritage objects.