Perceptual Quality Evaluation of 3D Triangle Mesh: A Technical Review

© 2018 IEEE. During mesh processing operations (e.g. simplifications, compression, and watermarking), a 3D triangle mesh is subject to various visible distortions on mesh surface which result in a need to estimate visual quality. The necessity of perceptual quality evaluation is already established, as in most cases, human beings are the end users of 3D meshes. To measure such kinds of distortions, the metrics that consider geometric measures integrating human visual system (HVS) is called perceptual quality metrics. In this paper, we direct an expansive study on 3D mesh quality evaluation mostly focusing on recently proposed perceptual based metrics. We limit our study on greyscale static mesh evaluation and attempt to figure out the most workable method for real-Time evaluation by making a quantitative comparison. This paper also discusses in detail how to evaluate objective metric's performance with existing subjective databases. In this work, we likewise research the utilization of the psychometric function to expel non-linearity between subjective and objective values. Finally, we draw a comparison among some selected quality metrics and it shows that curvature tensor based quality metrics predicts consistent result in terms of correlation

No-Reference Quality Assessment for Colored Point Cloud and Mesh Based on Natural Scene Statistics

To improve the viewer's quality of experience and optimize processing systems in computer graphics applications, the 3D quality assessment (3D-QA) has become an important task in the multimedia area. Point cloud and mesh are the two most widely used electronic representation formats of 3D models, the quality of which is quite sensitive to operations like simplification and compression. Therefore, many studies concerning point cloud quality assessment (PCQA) and mesh quality assessment (MQA) have been carried out to measure the visual quality degradations caused by lossy operations. However, a large part of previous studies utilizes full-reference (FR) metrics, which means they may fail to predict the accurate quality level of 3D models when the reference 3D model is not available. Furthermore, limited numbers of 3D-QA metrics are carried out to take color features into consideration, which significantly restricts the effectiveness and scope of application. In many quality assessment studies, natural scene statistics (NSS) have shown a good ability to quantify the distortion of natural scenes to statistical parameters. Therefore, we propose an NSS-based no-reference quality assessment metric for colored 3D models. In this paper, quality-aware features are extracted from the aspects of color and geometry directly from the 3D models. Then the statistic parameters are estimated using different distribution models to describe the characteristic of the 3D models. Our method is mainly validated on the colored point cloud quality assessment database (SJTU-PCQA) and the colored mesh quality assessment database (CMDM). The experimental results show that the proposed method outperforms all the state-of-art NR 3D-QA metrics and obtains an acceptable gap with the state-of-art FR 3D-QA metrics

A perceptual quality metric for 3D triangle meshes based on spatial pooling

© 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. In computer graphics, various processing operations are applied to 3D triangle meshes and these processes often involve distortions, which affect the visual quality of surface geometry. In this context, perceptual quality assessment of 3D triangle meshes has become a crucial issue. In this paper, we propose a new objective quality metric for assessing the visual difference between a reference mesh and a corresponding distorted mesh. Our analysis indicates that the overall quality of a distorted mesh is sensitive to the distortion distribution. The proposed metric is based on a spatial pooling strategy and statistical descriptors of the distortion distribution. We generate a perceptual distortion map for vertices in the reference mesh while taking into account the visual masking effect of the human visual system. The proposed metric extracts statistical descriptors from the distortion map as the feature vector to represent the overall mesh quality. With the feature vector as input, we adopt a support vector regression model to predict the mesh quality score.We validate the performance of our method with three publicly available databases, and the comparison with state-of-the-art metrics demonstrates the superiority of our method. Experimental results show that our proposed method achieves a high correlation between objective assessment and subjective scores

GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment

Nowadays, most 3D model quality assessment (3DQA) methods have been aimed at improving performance. However, little attention has been paid to the computational cost and inference time required for practical applications. Model-based 3DQA methods extract features directly from the 3D models, which are characterized by their high degree of complexity. As a result, many researchers are inclined towards utilizing projection-based 3DQA methods. Nevertheless, previous projection-based 3DQA methods directly extract features from multi-projections to ensure quality prediction accuracy, which calls for more resource consumption and inevitably leads to inefficiency. Thus in this paper, we address this challenge by proposing a no-reference (NR) projection-based \textit{\underline{G}rid \underline{M}ini-patch \underline{S}ampling \underline{3D} Model \underline{Q}uality \underline{A}ssessment (GMS-3DQA)} method. The projection images are rendered from six perpendicular viewpoints of the 3D model to cover sufficient quality information. To reduce redundancy and inference resources, we propose a multi-projection grid mini-patch sampling strategy (MP-GMS), which samples grid mini-patches from the multi-projections and forms the sampled grid mini-patches into one quality mini-patch map (QMM). The Swin-Transformer tiny backbone is then used to extract quality-aware features from the QMMs. The experimental results show that the proposed GMS-3DQA outperforms existing state-of-the-art NR-3DQA methods on the point cloud quality assessment databases. The efficiency analysis reveals that the proposed GMS-3DQA requires far less computational resources and inference time than other 3DQA competitors. The code will be available at https://github.com/zzc-1998/GMS-3DQA

On the Real-Time Performance, Robustness and Accuracy of Medical Image Non-Rigid Registration

Three critical issues about medical image non-rigid registration are performance, robustness and accuracy. A registration method, which is capable of responding timely with an accurate alignment, robust against the variation of the image intensity and the missing data, is desirable for its clinical use. This work addresses all three of these issues. Unacceptable execution time of Non-rigid registration (NRR) often presents a major obstacle to its routine clinical use. We present a hybrid data partitioning method to parallelize a NRR method on a cooperative architecture, which enables us to get closer to the goal: accelerating using architecture rather than designing a parallel algorithm from scratch. to further accelerate the performance for the GPU part, a GPU optimization tool is provided to automatically optimize GPU execution configuration.;Missing data and variation of the intensity are two severe challenges for the robustness of the registration method. A novel point-based NRR method is presented to resolve mapping function (deformation field) with the point correspondence missing. The novelty of this method lies in incorporating a finite element biomechanical model into an Expectation and Maximization (EM) framework to resolve the correspondence and mapping function simultaneously. This method is extended to deal with the deformation induced by tumor resection, which imposes another challenge, i.e. incomplete intra-operative MRI. The registration is formulated as a three variable (Correspondence, Deformation Field, and Resection Region) functional minimization problem and resolved by a Nested Expectation and Maximization framework. The experimental results show the effectiveness of this method in correcting the deformation in the vicinity of the tumor. to deal with the variation of the intensity, two different methods are developed depending on the specific application. For the mono-modality registration on delayed enhanced cardiac MRI and cine MRI, a hybrid registration method is designed by unifying both intensity- and feature point-based metrics into one cost function. The experiment on the moving propagation of suspicious myocardial infarction shows effectiveness of this hybrid method. For the multi-modality registration on MRI and CT, a Mutual Information (MI)-based NRR is developed by modeling the underlying deformation as a Free-Form Deformation (FFD). MI is sensitive to the variation of the intensity due to equidistant bins. We overcome this disadvantage by designing a Top-to-Down K-means clustering method to naturally group similar intensities into one bin. The experiment shows this method can increase the accuracy of the MI-based registration.;In image registration, a finite element biomechanical model is usually employed to simulate the underlying movement of the soft tissue. We develop a multi-tissue mesh generation method to build a heterogeneous biomechanical model to realistically simulate the underlying movement of the brain. We focus on the following four critical mesh properties: tissue-dependent resolution, fidelity to tissue boundaries, smoothness of mesh surfaces, and element quality. Each mesh property can be controlled on a tissue level. The experiments on comparing the homogeneous model with the heterogeneous model demonstrate the effectiveness of the heterogeneous model in improving the registration accuracy

Sparse Bases and Bayesian Inference of Electromagnetic Scattering

Many approaches in CEM rely on the decomposition of complex radiation and scattering behavior with a set of basis vectors. Accurate estimation of the quantities of interest can be synthesized through a weighted sum of these vectors. In addition to basis decompositions, sparse signal processing techniques developed in the CS community can be leveraged when only a small subset of the basis vectors are required to sufficiently represent the quantity of interest. We investigate several concepts in which novel bases are applied to common electromagnetic problems and leverage the sparsity property to improve performance and/or reduce computational burden. The first concept explores the use of multiple types of scattering primitives to reconstruct scattering patterns of electrically large targets. Using a combination of isotropic point scatterers and wedge diffraction primitives as our bases, a 40% reduction in reconstruction error can be achieved. Next, a sparse basis is used to improve DOA estimation. We implement the BSBL technique to determine the angle of arrival of multiple incident signals with only a single snapshot of data from an arbitrary arrangement of non-isotropic antennas. This is an improvement over the current state-of-the-art, where restrictions on the antenna type, configuration, and a priori knowledge of the number of signals are often assumed. Lastly, we investigate the feasibility of a basis set to reconstruct the scattering patterns of electrically small targets. The basis is derived from the TCM and can capture non-localized scattering behavior. Preliminary results indicate that this basis may be used in an interpolation and extrapolation scheme to generate scattering patterns over multiple frequencies

Multidisciplinary Design Optimization for Sonic Boom Mitigation

Automated, parallelized, time-efficient surface definition and grid generation and flow simulation methods are developed for sharp and accurate sonic boom signal computation in three dimensions in the near and mid-field of an aircraft using Euler/Full-Potential unstructured/structured computational fluid dynamics. The full-potential mid-field sonic boom prediction code is an accurate and efficient solver featuring automated grid generation, grid adaptation and shock fitting, and parallel processing. This program quickly marches the solution using a single nonlinear equation for large distances that cannot be covered with Euler solvers due to large memory and long computational time requirements. The solver takes into account variations in temperature and pressure with altitude. The far-field signal prediction is handled using the classical linear Thomas Waveform Parameter Method where the switching altitude from the nonlinear to linear prediction is determined by convergence of the ground signal pressure impulse value. This altitude is determined as r/L ≈ 10 from the source for a simple lifting wing, and r/L ≈ 40 for a real complex aircraft. Unstructured grid adaptation and shock fitting methodology developed for the near-field analysis employs an Hessian based anisotropic grid adaptation based on error equidistribution. A special field scalar is formulated to be used in the computation of the Hessian based error metric which enhances significantly the adaptation scheme for shocks. The entire cross-flow of a complex aircraft is resolved with high fidelity using only 500,000 grid nodes after only about 10 solution/adaptation cycles. Shock fitting is accomplished using Roe\u27s Flux-Difference Splitting scheme which is an approximate Riemann type solver and by proper alignment of the cell faces with respect to shock surfaces. Simple to complex real aircraft geometries are handled with no user-interference required making the simulation methods suitable tools for product design. The simulation tools are used to optimize three geometries for sonic boom mitigation. The first is a simple axisymmetric shape to be used as a generic nose component, the second is a delta wing with lift, and the third is a real aircraft with nose and wing optimization. The objectives are to minimize the pressure impulse or the peak pressure in the sonic boom signal, while keeping the drag penalty under feasible limits. The design parameters for the meridian profile of the nose shape are the lengths and the half-cone angles of the linear segments that make up the profile. The design parameters for the lifting wing are the dihedral angle, angle of attack, non-linear span-wise twist and camber distribution. The test-bed aircraft is the modified F-5E aircraft built by Northrop Grumman, designated the Shaped Sonic Boom Demonstrator. This aircraft is fitted with an optimized axisymmetric nose, and the wings are optimized to demonstrate optimization for sonic boom mitigation for a real aircraft. The final results predict 42% reduction in bow shock strength, 17% reduction in peak Δp, 22% reduction in pressure impulse, 10% reduction in foot print size, 24% reduction in inviscid drag, and no loss in lift for the optimized aircraft. Optimization is carried out using response surface methodology, and the design matrices are determined using standard DoE techniques for quadratic response modeling

Parametric modeling for simulation based hypersonic vehicle design

The conceptual design stage offers the most opportunity for innovation and the capability to reveal costly design errors early. Integrating high fidelity design and simulation tools into the conceptual design stage enables engineers to develop design variations quickly and affordably. This work focuses primarily on the development and utilization of parametric modeling methods as they apply to a simulation based design process. It will also address the impacts to conceptual design development time. A blended wing-body (BWB) hypersonic wave rider demonstrates how state-of-the-art solid modeling techniques can be coupled to high fidelity CFD analysis codes to perform top down design. Performance trends are identified for several trade study variations which represent a single iteration through the simulation based design process. Performance metrics are based on interpretations from higher level customer, regulatory, business, and other requirements. The process of cascading these requirements down to the component level is the definition of top-down-design. This bidirectional tracing of requirements allows vehicle development to progress in a manner such that any change of the vehicle can be assessed in terms of the overarching requirements

Enabling technology for non-rigid registration during image-guided neurosurgery

In the context of image processing, non-rigid registration is an operation that attempts to align two or more images using spatially varying transformations. Non-rigid registration finds application in medical image processing to account for the deformations in the soft tissues of the imaged organs. During image-guided neurosurgery, non-rigid registration has the potential to assist in locating critical brain structures and improve identification of the tumor boundary. Robust non-rigid registration methods combine estimation of tissue displacement based on image intensities with the spatial regularization using biomechanical models of brain deformation. In practice, the use of such registration methods during neurosurgery is complicated by a number of issues: construction of the biomechanical model used in the registration from the image data, high computational demands of the application, and difficulties in assessing the registration results. In this dissertation we develop methods and tools that address some of these challenges, and provide components essential for the intra-operative application of a previously validated physics-based non-rigid registration method.;First, we study the problem of image-to-mesh conversion, which is required for constructing biomechanical model of the brain used during registration. We develop and analyze a number of methods suitable for solving this problem, and evaluate them using application-specific quantitative metrics. Second, we develop a high-performance implementation of the non-rigid registration algorithm and study the use of geographically distributed Grid resources for speculative registration computations. Using the high-performance implementation running on the remote computing resources we are able to deliver the results of registration within the time constraints of the neurosurgery. Finally, we present a method that estimates local alignment error between the two images of the same subject. We assess the utility of this method using multiple sources of ground truth to evaluate its potential to support speculative computations of non-rigid registration