Surface Representation Research Articles

EXPERIMENTING TIN DATA STRUCTURE FOR REPRESENTING PLANE SURFACE AND SUBSURFACE SPATIAL OBJECTS - PRELIMINARY WORK

Abstract. Understanding and analyzing complex spatial objects involves the integration of surface, terrain, and subsurface data into 3D spatial models. However, describing and efficiently analyzing the connections between these data model is particularly challenging. This study aims to generate TIN data structure that enable the 3D representation of surface terrain subsurface integration. Furthermore, to ensure accurate representation within a unified 3D model, spatial relationships between the TIN data model could be obtained. Consequently, three – dimensional Triangular Irregular Networks (3D TIN) mesh of spatial objects representations and indexing structures were created. The integration of spatial objects, encompassing surface, terrain, and subsurface elements, is realized through a unified model. This synergy is achieved by combining the distinct 2D and 3D topology structures of each component. The resulting comprehensive model captures the intricate relationships and interactions within the spatial environment. This study provides additional insight into the representation and analysis of surface, terrain, and subsurface management in 3D spatial models. Hence, better decision making, resource management, underground utility installations, and evaluation of spatial objects will be made possible in our future work.

Ocular surface information seen from the somatosensory thalamus and cortex.

Ocular Surface (OS) somatosensory innervation detects external stimuli producing perceptions, such as pain or dryness, the most relevant symptoms in many OS pathologies. Nevertheless, little is known about the central nervous system circuits involved in these perceptions, and how they integrate multimodal inputs in general. Here, we aim to describe the thalamic and cortical activity in response to OS stimulation of different modalities. Electrophysiological extracellular recordings in anaesthetized rats were used to record neural activity, while saline drops at different temperatures were applied to stimulate the OS. Neurons were recorded in the ophthalmic branch of the trigeminal ganglion (TG, 49 units), the thalamic VPM-POm nuclei representing the face (Th, 69 units) and the primary somatosensory cortex (S1, 101 units). The precise locations for Th and S1 neurons receiving OS information are reported here for the first time. Interestingly, all recorded nuclei encode modality both at the single neuron and population levels, with noxious stimulation producing a qualitatively different activity profile from other modalities. Moreover, neurons responding to new combinations of stimulus modalities not present in the peripheral TG subsequently appear in Th and S1, being organized in space through the formation of clusters. Besides, neurons that present higher multimodality display higher spontaneous activity. These results constitute the first anatomical and functional characterization of the thalamocortical representation of the OS. Furthermore, they provide insight into how information from different modalities gets integrated from the peripheral nervous system into the complex cortical networks of the brain. KEY POINTS: Anatomical location of thalamic and cortical ocular surface representation. Thalamic and cortical neuronal responses to multimodal stimulation of the ocular surface. Increasing functional complexity along trigeminal neuroaxis. Proposal of a new perspective on how peripheral activity shapes central nervous system function.

An Ongoing Tonal-Pattern Change: Lanzhou Dialect

The tonal-pattern of Lanzhou dialect is experiencing a change demonstrated in two aspects: a) Yin-ping 阴平 (T1a) has two variants, falling and level, and the former is being replaced by the latter; b) Shang-sheng 上声 (T2) and Qu-sheng 去声 (T3) are merging. We propose that the tonal change of T1a is partially a result of language contact, and the sandhi T1a from initial position of disyllabic combination also plays a role in extending the level tone to the citation T1a, the change of T1a then further triggers the merger of T2 and T3. Yet whether it is the change of sandhi T1a in the initial position or the change of the surface representation of citation T1a, or the merger of T2 and T3, all the directionality of tonal change is in line with the “clockwise tone shift circle” (Zhu et al. 2015; Yang and Xu 2019). We argue that tone changes are mainly due to internal factors. Although there are external stimuli, the path of tone change follows rules. The illustration of the ongoing tonal-pattern change in Lanzhou dialect will shed light on the understanding of the rules.

Vox-Surf: Voxel-Based Implicit Surface Representation.

Virtual content creation and interaction play an important role in modern 3D applications. Recovering detailed 3D models from real scenes can significantly expand the scope of its applications and has been studied for decades in the computer vision and computer graphics community. In this work, we propose Vox-Surf, a voxel-based implicit surface representation. Our Vox-Surf divides the space into finite sparse voxels, where each voxel is a basic geometry unit that stores geometry and appearance information on its corner vertices. Due to the sparsity inherited from the voxel representation, Vox-Surf is suitable for almost any scene and can be easily trained end-to-end from multiple view images. We utilize a progressive training process to gradually cull out empty voxels and keep only valid voxels for further optimization, which greatly reduces the number of sample points and improves inference speed. Experiments show that our Vox-Surf representation can learn fine surface details and accurate colors with less memory and faster rendering than previous methods. The resulting fine voxels can also be considered as the bounding volumes for collision detection, which is useful in 3D interactions. We also show the potential application of Vox-Surf in scene editing and augmented reality. The source code is publicly available at https://github.com/zju3dv/Vox-Surf.

SENSAAS-Flex: a joint optimization approach for aligning 3D shapes and exploring the molecular conformation space.

Popular shape-based alignment methods handle molecular flexibility by utilizing conformational ensembles to select the most fitted conformer. However, the initial conformer library generation step is computationally intensive and limiting to the overall alignment process. In this work, we describe a method to perform flexible alignment of two molecular shapes by optimizing the 3D conformation. SENSAAS-Flex, an add-on to the SENSAAS tool, is able to proceed from a limited set of initial conformers through an iterative process where additional conformational optimizations are made at the substructure level and constrained by the target shape. In self- and cross-alignment experiments, SENSAAS-Flex is able to reproduce the crystal structure geometry of ligands of the AstraZeneca Molecule Overlay Test set and PDBbind refined dataset. Our study shows that the point-based representation of molecular surfaces is appropriate in terms of shape constraint to sample the conformational space and perform flexible molecular alignments. The documentation and source code are available at https://chemoinfo.ipmc.cnrs.fr/Sensaas-flex/sensaas-flex-main.tar.gz.

A novel framework for detection and prevention of denial of service attacks on autonomous vehicles using fuzzy logic

Every year, millions of people lose their lives due to road accidents, and countless others suffer from severe injuries. Moreover, these accidents cause economic losses worldwide. Primary reasons for these accidents include over speeding, doziness, distracted driving, drugs, and psychoactive substances. Many efforts are made to automate vehicles to save losses. Autonomous vehicles are expected to revolutionize transportation by reducing the likelihood of accidents resulting from human mistakes, increasing efficiency, and reducing congestion. Like any other computer-based system, autonomous vehicles can be susceptible to attacks that could compromise their safety, security, and privacy. Denial-of-Service (DoS) attack is a cyberattack that aims to disturb a network's typical or standard functioning. A successful DoS attack could cause the autonomous vehicle to malfunction or stop functioning altogether, leading to accidents or other safety risks. To mitigate the risk of DoS attacks, protective measures that have been proposed include encryption, firewalls, and intrusion detection systems. The previous approaches to detect and prevent Denial-of-Service (DoS) attacks in autonomous vehicles are associated with imprecise and inaccurate results and high computational costs. The main goal of this research is to present a novel approach that utilizes fuzzy logic to effectively detect and mitigate Denial of Service (DoS) attacks. The proposed approach has achieved 99.4% detection and prevention rate by attaining optimal levels of security with testing error upto 0.6%. Furthermore, comprehensive execution and validation of security measures have been conducted by adopting the attack surface and rules representation. The presented approach delivers more precise and accurate results while maintaining a lower computational cost than existing methodologies. In future the proposed scheme can be used for detection of other attacks in autonomous vehicles by incorporating relevant input variables and their respective ranges.

A new approach to three-dimensional microstructure reconstruction of a polycrystalline solar cell using high-efficiency Cu(In,Ga)Se2

A new method for efficiently converting electron backscatter diffraction data obtained using serial sectioning by focused ion beam of a polycrystalline thin film into a computational, three-dimensional (3D) structure is presented. The reported data processing method results in a more accurate representation of the grain surfaces, reduced computer memory usage, and improved processing speed compared to traditional voxel methods. The grain structure of a polycrystalline absorption layer from a high-efficiency Cu(In,Ga)Se2 solar cell (19.5%) is reconstructed in 3D and the grain size and surface distribution is investigated. The grain size distribution is found to be best fitted by a log-normal distribution. We further find that the grain size is determined by the [Ga]/([Ga] + [In]) ratio in vertical direction, which was measured by glow discharge optical emission spectroscopy. Finally, the 3D model derived from the structural information is applied in optoelectronic simulations, revealing insights into the effects of grain boundary recombination on the open-circuit voltage of the solar cell. An accurate 3D structure like the one obtained with our method is a prerequisite for a detailed understanding of mechanical properties and for advanced optical and electronic simulations of polycrystalline thin films.

Improving torque-tension relationship in bolted joints: accurate threaded surface representation and pressure distribution considerations

The torque-tension relationship plays a vital role in the application of bolted joints, providing essential guidance not only in controlling the preload during the tightening process but also serving as the theoretical basis for measuring the coefficient of friction on threaded surfaces. In order to develop a better understanding of the torque-tension relationship, the force and torque on the contact surface during the preload process were analyzed using a differential geometric representation of the thread surface. This representation took into account three-dimensional effects of the thread surface, particularly its non-developability. The hexahedral meshed model of the bolted joints was used in the FEA of the tightening process to obtain the pressure distribution on the contact surfaces. Based on the accurate representation of thread surface and consideration of pressure distribution forms, a novel torque-tension relationship was proposed and compared with current relationships in the literature. The error analysis showed that the proposed formulation has lower relative errors with the FEA results than those in the literature, indicating a higher level of accuracy. This proposed torque-tension relationship not only improves the safety and reliability of bolted joints, but also contributes to a better understanding of thread mechanics during tightening process.

A GPU accelerated three-dimensional ghost cell method with an improved implicit surface representation for complex rigid or flexible boundary flows

This paper presents an efficient and reliable three-dimensional (3D) ghost cell method for complex rigid or flexible boundary flows. An improved implicit interface representation method is developed to treat the arbitrary complex interfaces including the thin or sharp boundaries. This method is robust and avoids the solving of a large dense matrix, and thus the computational efficiency is greatly enhanced. The ghost cell method with a unified eight-node interpolation scheme is adopted to enforce the no-slip boundary conditions on complex boundaries. To perform 3D high-resolution simulation on a desktop computer, a GPU parallel framework in CUDA environment is developed to significantly accelerate the computational efficiency of the present solver. The horizontal oscillations of a two-dimensional (2D) cylinder and a 3D sphere in the rest fluid are simulated to validate the accuracy and computational performance of the present model. Spatial accuracy of near second-order on the boundary treatment and good grid convergence are confirmed. Also, the present solver can achieve hundreds of acceleration ratios for 2D grids and thousands of acceleration ratios for 3D grids. Then, uniform flows around a 3D arbitrary rigid or flexible object such as a stationary cylindrical cylinder, a stationary square cylinder, an oscillating airfoil, and a swimming carangiform are simulated. The accuracy and capability of the present model for 3D complex geometries with large-amplitude movement or deformation is satisfactorily validated. Moreover, typical 3D wake structures are well captured.

GPU-accelerated lung CT segmentation based on level sets and texture analysis

This paper presents a novel semi-automatic method for lung segmentation in thoracic CT datasets. The fully three-dimensional algorithm is based on a level set representation of an active surface and integrates texture features to improve its robustness. The method’s performance is enhanced by the graphics processing unit (GPU) acceleration. The segmentation process starts with a manual initialisation of 2D contours on a few representative slices of the analysed volume. Next, the starting regions for the active surface are generated according to the probability maps of texture features. The active surface is then evolved to give the final segmentation result. The recent implementation employs features based on grey-level co-occurrence matrices and Gabor filters. The algorithm was evaluated on real medical imaging data from the LCTCS 2017 challenge. The results were also compared with the outcomes of other segmentation methods. The proposed approach provided high segmentation accuracy while offering very competitive performance.

PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction

Abstract3D surface reconstruction from point clouds is a key step in areas such as content creation, archaeology, digital cultural heritage and engineering. Current approaches either try to optimize a non‐data‐driven surface representation to fit the points, or learn a data‐driven prior over the distribution of commonly occurring surfaces and how they correlate with potentially noisy point clouds. Data‐driven methods enable robust handling of noise and typically either focus on a global or a local prior, which trade‐off between robustness to noise on the global end and surface detail preservation on the local end. We propose PPSurf as a method that combines a global prior based on point convolutions and a local prior based on processing local point cloud patches. We show that this approach is robust to noise while recovering surface details more accurately than the current state‐of‐the‐art. Our source code, pre‐trained model and dataset are available at https://github.com/cg‐tuwien/ppsurf.

Organization of Somatosensory Cortex in the South American Rodent Paca (Cuniculus paca)

Introduction: The study of non-laboratory species has been part of a broader effort to establish the basic organization of the mammalian neocortex, as these species may provide unique insights relevant to cortical organization, function, and evolution. Methods: In the present study, the organization of three somatosensory cortical areas of the medium-sized (5–11 kg body mass) Amazonian rodent, the paca (Cuniculus paca), was determined using a combination of electrophysiological microelectrode mapping and histochemical techniques (cytochrome oxidase and NADPH diaphorase) in tangential sections. Results: Electrophysiological mapping revealed a somatotopically organized primary somatosensory cortical area (S1) located in the rostral parietal cortex with a characteristic foot-medial/head-lateral contralateral body surface representation similar to that found in other species. S1 was bordered laterally by two regions housing neurons responsive to tactile stimuli, presumably the secondary somatosensory (S2) and parietal ventral (PV) cortical areas that evinced a mirror-reversal representation (relative to S1) of the contralateral body surface. The limits of the putative primary visual (V1) and primary auditory (A1) cortical areas, as well as the complete representation of the contralateral body surface in S1, were determined indirectly by the histochemical stains. Like the barrel field described in small rodents, we identified a modular arrangement located in the face representation of S1. Conclusions: The relative location, somatotopic organization, and pattern of neuropil histochemical reactivity in the three paca somatosensory cortical areas investigated are similar to those described in other mammalian species, providing additional evidence of a common plan of organization for the somatosensory cortex in the rostral parietal cortex of mammals.

Surface-SOS: Self-Supervised Object Segmentation via Neural Surface Representation.

Self-supervised Object Segmentation (SOS) aims to segment objects without any annotations. Under conditions of multi-camera inputs, the structural, textural and geometrical consistency among each view can be leveraged to achieve fine-grained object segmentation. To make better use of the above information, we propose Surface representation based Self-supervised Object Segmentation (Surface-SOS), a new framework to segment objects for each view by 3D surface representation from multi-view images of a scene. To model high-quality geometry surfaces for complex scenes, we design a novel scene representation scheme, which decomposes the scene into two complementary neural representation modules respectively with a Signed Distance Function (SDF). Moreover, Surface-SOS is able to refine single-view segmentation with multi-view unlabeled images, by introducing coarse segmentation masks as additional input. To the best of our knowledge, Surface-SOS is the first self-supervised approach that leverages neural surface representation to break the dependence on large amounts of annotated data and strong constraints. These constraints typically involve observing target objects against a static background or relying on temporal supervision in videos. Extensive experiments on standard benchmarks including LLFF, CO3D, BlendedMVS, TUM and several real-world scenes show that Surface-SOS always yields finer object masks than its NeRF-based counterparts and surpasses supervised single-view baselines remarkably. Code is available at: https://github.com/zhengxyun/Surface-SOS.

Some Bounded Linear and Compact Operators into the Generalised Hahn Space

We characterise some classes of bounded linear and compact operators from the spaces of sequences that are strongly summable or strongly summable to zero with index p ≥ 1 by the Cesaro method of order 1 into the generalised Hahn space. Furthermore, we establish estimates for the norm and the Hausdorff measure of noncompactness of those operators. Our results are complementary to recently published dual corresponding results with the order of the sequence spaces reversed. We also apply our results to the graphical representations of related potential surfaces and the corresponding Wulff’s crystals.

An Optimization Method of Assembly Process for Bolt Flange Connection Structure with Manufacturing Characteristics

Threaded connections are a common method in precision assembly. As threaded connections in precision assembly are rigid, the morphology of contact surfaces and their stress states significantly influence the alignment accuracy and stability of the connection structure. Establishing a quantified intelligent decision-making approach from connection performance requirements to connection assembly is of crucial significance for enhancing product performance. In this paper, a precision measuring machine is used to acquire point cloud data of the circular contact surfaces of two flanges. Subsequently, a NURBS surface modeling method is employed to construct a three-dimensional representation of the flange contact surface with manufacturing characteristics. The attitude vectors of the connection structures on both sides of the flange contact surface are established. A multi-objective optimization method is employed to adjust the installation angles, optimizing the combined state of contact status and connection attitude to achieve the optimal configuration. The contact area is divided based on bolt positions and quantities, and an optimized pretension force for bolts within each area is determined using a simplified contact model for that region. ABAQUS simulation software is used to numerically solve the bolt flange connection structure before and after optimization under long-term temperature cycles. Through simulation, the positional accuracy of the bolt flange connection structure related to assembly is improved by 52.7% compared to the pre-optimized state, and the long-term positional accuracy is enhanced by 23.5%.

HSDF: Hybrid Sign and Distance Field for Neural Representation of Surfaces with Arbitrary Topologies.

Neural implicit function based on signed distance field (SDF) has achieved impressive progress in reconstructing 3D models with high fidelity. However, such approaches can only represent closed surfaces. Recent works based on unsigned distance function (UDF) are proposed to handle both watertight and single-layered open surfaces. Nonetheless, as UDF is signless, its direct output is limited to the point cloud, which imposes an additional challenge on extracting high-quality meshes from discrete points. To address this challenge, we present a novel neural implicit representation coded HSDF, which is a hybrid of signed and unsigned distance fields. In particular, HSDF is able to represent arbitrary topologies containing both closed and open surfaces while being compatible with existing iso-surface extraction techniques for easy field-to-mesh conversion. In addition to predicting a UDF, we propose to learn an additional sign field. Unlike traditional SDF, HSDF is able to locate the surface of interest before level surface extraction by generating surface points following NDF[1]. We are then able to obtain open surfaces via an adaptive meshing approach that only instantiates regions containing surfaces into a polygon mesh. HSDF benefits downstream tasks like neural rendering, as it enables the rendering of back-faces of open surfaces. We also propose HSDF-Net, a dedicated learning framework that factorizes the learning of HSDF into two easier sub-problems. Experiments and evaluations show that HSDF outperforms the state-of-the-art techniques both qualitatively and quantitatively on some of the used datasets.

A Novel Radial Basis Function Description of a Smooth Implicit Surface for Musculoskeletal Modelling

As musculoskeletal illnesses continue to increase, practical computerised muscle modelling is crucial. This paper addresses this concern by proposing a mathematical model for a dynamic 3D geometrical surface representation of muscles using a Radial Basis Function (RBF) approximation technique. The objective is to obtain a smoother surface while minimising data use, contrasting it from classical polygonal (e.g. triangular) surface mesh models or volumetric (e.g. tetrahedral) mesh models. The paper uses RBF implicit surface description to describe static surface generation and dynamic surface deformations based on its spatial curvature preservation during the deformation. The novel method is tested on multiple data sets, and the experiments show promising results according to the introduced metrics.

DeepMesh: Differentiable Iso-Surface Extraction.

Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D euclidean grid, resulting in a learnable parameterization that is unlimited in resolution. Unfortunately, these methods are often unsuitable for applications that require an explicit mesh-based surface representation because converting an implicit field to such a representation relies on the Marching Cubes algorithm, which cannot be differentiated with respect to the underlying implicit field. In this work, we remove this limitation and introduce a differentiable way to produce explicit surface mesh representations from Deep Implicit Fields. Our key insight is that by reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3D location of surface samples with respect to the underlying deep implicit field. We exploit this to define DeepMesh - an end-to-end differentiable mesh representation that can vary its topology. We validate our theoretical insight through several applications: Single view 3D Reconstruction via Differentiable Rendering, Physically-Driven Shape Optimization, Full Scene 3D Reconstruction from Scans and End-to-End Training. In all cases our end-to-end differentiable parameterization gives us an edge over state-of-the-art algorithms.

ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials

The emergence of artificial intelligence is profoundly impacting computational chemistry, particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional potential energy surface representations, MLIPs surpass the conventional computational scaling limitations by...

Freetures: Localization in Signed Distance Function Maps

Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps. Existing techniques for geometry-based localization have focused on the description of local surface geometry, usually using pointclouds as the underlying representation. We propose a system for geometrybased localization that extracts features directly from an implicit surface representation: the Signed Distance Function (SDF). The SDF varies continuously through space, which allows the proposed system to extract and utilize features describing both surfaces and free-space. Through evaluations on public datasets, we demonstrate the flexibility of this approach, and show an increase in localization performance over state-of-the-art handcrafted surfaces-only descriptors. We achieve an average improvement of ~12% on an RGB-D dataset and ~18% on a LiDAR-based dataset. Finally, we demonstrate our system for localizing a LiDAR-equipped Micro Aerial Vehicle (MAV) within a previously built map of a search and rescue training ground. Our system, called freetures, is available as open source <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$^{1}$</tex></formula> .