Explicit Surface Representation Research Articles

PySubdiv 1.0: open-source geological modeling and reconstruction by non-manifold subdivision surfaces

Abstract. Sealed geological models are commonly used as an input to process simulations, for example, in hydrogeological or geomechanical studies. Creating these meshes often requires tedious manual work, and it is therefore difficult to adjust a once-created model. In this work, we propose a flexible framework to create and interact with geological models using explicit surface representations. The essence of the work lies in the determination of the control mesh and the definition of semi-sharp-crease values, which, in combination, enable the representation of complex structural settings with a low number of control points. We achieve this flexibility through the adaptation of recent algorithms from the field of computer graphics to the specific requirements of geological modeling, specifically the representation of non-manifold topologies and sharp features. We combine the method with a particle swarm optimization (PSO) approach to enable the automatic optimization of vertex position and crease sharpness values. The result of this work is implemented in an open-source software (PySubdiv) for reconstructing geological structures while resulting in a model which is (1) sealed/watertight, (2) controllable with a control mesh and (3) topologically similar to the input geological structure. Also, the reconstructed model may include a lower number of vertices compared to the input geological structure, which results in reducing the cost of modeling and simulation. In addition to enabling a manual adjustment of sealed geological models, the algorithm also provides a method for the integration of explicit surface representations in inverse frameworks and the consideration of uncertainties.

Enhanced heat source modeling in particle-based laser manufacturing simulations with ray tracing

Particle-based spatial discretization methods such as smoothed particle hydrodynamics (SPH) possess suitability for the high-fidelity simulation of laser manufacturing processes due to their capability in treating complex free-surface and multiphase problems. In such simulations, laser heat source modeling remains a central concern as the mechanisms governing the material deformations and phase changes strongly depend on the input laser energy and its distribution details. Ray tracing (RT) is a realistic and accurate approach for high-fidelity heat source modeling that can capture surface heterogeneity and multiple reflections. However, RT heat source modeling for particle-based numerical methods faces additional challenges rooted in the lack of explicit surface representation, intensive computational efforts, and implementation complexities. This work addresses these issues by proposing an enhanced and efficient RT heat source model for particle-based laser manufacturing simulations with negligible computational overhead and without requiring background nodes or surface mesh reconstruction. After validating the RT heat source model versus analytical and experimental results, we assess its computational performance through two test cases, showing that the computing workload imposed by our RT model is between 0.1% and 25% of the total runtime in different SPH-based laser manufacturing simulations. With the achieved efficiency and robustness, it becomes convenient yet advantageous to simulate processes like laser drilling and laser powder bed fusion with precision far beyond the conventional approaches using volumetric heat sources.

Geometric advection and its application in the emulation of high aspect ratio structures

The ability to simulate the processes required to fabricate advanced microelectronic structures, commonly referred to as process technology computer aided design (TCAD), is essential for the semiconductor industry. It aids in the design and development of modern integrated circuits at low cost. Ongoing demands of high efficiency and simplicity lead to the development of process emulation techniques, which describe the effect of a fabrication step by using only the geometric properties gathered from experimental data or extracted from physical simulations. However, the accuracy of current emulation models is limited by their strong reliance on explicit surface representations, while traditional algorithms employing implicit surfaces, such as the level set, can frequently suffer from numerical limitations. A novel geometric advection algorithm for microelectronic process emulation is presented, which advances a level set surface over large distances in a single simulation step. It combines the advantages of explicit surface representations, such as fast access to surface data and efficient sorting, and the robustness of implicit surface representations. Furthermore, calculations are performed directly in the level set avoiding costly conversions to explicit surfaces. The performance and applicability of the algorithm to modern processes is demonstrated by emulating the fabrication of several high aspect ratio structures. Due to the almost universal applicability and computational efficiency of the presented geometric advection algorithm, it provides an entirely new way of emulating microelectronic fabrication processes and presents a promising alternative to long-standing and commonly used techniques.

Adaptive surface extraction from anisotropic volumetric data: contouring on generalized octrees

In this article, we present an algorithm to extract adaptive surfaces from anisotropic volumetric data. For example, this kind of data can be obtained from a set of segmented images from the sampling of an implicit function, or it can be built by using depth images produced by time-of-flight cameras. However, for many applications as geometry modeling, rendering, or finite elements, it is better to use an explicit surface representation. This surface must fit to the geometrical and topological features of the object in order to obtain a good approximation and to avoid topological artifacts. Our algorithm is able to extract adaptive surfaces that accurately approximate the geometry of the original object while minimizing aliasing effects. In addition, our solution is suitable to handle the anisotropy of volumetric representations. In comparison with relevant methods in the state of the art, ours offers a good compromise between mesh quality and precision in the geometrical approximation.

WarpCurves: A tool for explicit manipulation of implicit surfaces

We introduce WarpCurves, a technique for interactively manipulating an implicit surface using curve-based spatial deformations. Although implicit surfaces have several advantages in 3D modeling, current workflows are limited by the compositional nature of implicit modeling. Wide classes of surface features that are easy to create with the direct manipulation tools available for explicit surface representations are difficult to reproduce using volumetric implicit operations. We describe a novel spatial deformation that can be used to approximate direct surface manipulation. With our method an artist first draws a curve on the current surface to indicate the feature region-of-interest. Deformations applied to this handle curve are transferred to the implicit surface via an automatically constructed C 2 continuous space mapping. Additional curves can be added in a hierarchical manner to create complex shapes. Our technique is implemented as a node in the BlobTree hierarchical implicit volume representation, and hence can be used along with other volumetric nodes (operators) such as blending and CSG. Our results show that surface deformations which would be difficult to reproduce using existing volumetric operations can be quickly constructed using warp curves, making them a valuable addition to the implicit modeling toolbox.

Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia

A major challenge in functional neuroimaging is to cope with individual variability in cortical structure and function. Most analyses of cortical function compensate for variability using affine or low-dimensional nonlinear volume-based registration (VBR) of individual subjects to an atlas, which does not explicitly take into account the geometry of cortical convolutions. A promising alternative is to use surface-based registration (SBR), which capitalizes on explicit surface representations of cortical folding patterns in individual subjects. In this study, we directly compare results from SBR and affine VBR in a study of working memory in healthy controls and patients with schizophrenia (SCZ). Each subject's structural scan was used for cortical surface reconstruction using the SureFit method. fMRI data were mapped directly onto individual cortical surface models, and each hemisphere was registered to the population-average PALS-B12 atlas using landmark-constrained SBR. The precision with which cortical sulci were aligned was much greater for SBR than VBR. SBR produced superior alignment precision across the entire cortex, and this benefit was greater in patients with schizophrenia. We demonstrate that spatial smoothing on the surface provides better resolution and signal preservation than a comparable degree of smoothing in the volume domain. Lastly, the statistical power of functional activation in the working memory task was greater for SBR than for VBR. These results indicate that SBR provides significant advantages over affine VBR when analyzing cortical fMRI activations. Furthermore, these improvements can be even greater in disorders that have associated structural abnormalities.

An unbiased iterative group registration template for cortical surface analysis

Accurate alignment of explicit surface representations of human cerebral cortices is necessary in order to compare local individual differences in cortical morphometric measurements (thickness, surface area, gyrification, etc.) in both normal and clinical populations. This paper presents a methodology for developing unbiased, high resolution iterative registration templates from a group of 222 subject hemispheres and shows that the resulting template provides better alignment of a separate set of test data than single-subject templates. It demonstrates that between 30 and 50 subjects are required to generate a stable iterative template. It also explores the way in which fold variants in registration templates affect the quality of registration. Finally, it shows that hemisphere-specific group registration templates systematically better register subject hemispheres of the same laterality, underlining the need to develop templates free of hemisphere bias for asymmetry analysis.