Neural ShDF: Reviving an Efficient and Consistent Mesh Segmentation Method

Partitioning a polygonal mesh into meaningful parts can be challenging. Many applications require decomposing such structures for further processing in computer graphics. In the last decade, several methods were proposed to tackle this problem, at the cost of intensive computational times. Recently, machine learning has proven to be effective for the segmentation task on 3D structures. Nevertheless, these state-of-the-art methods are often hardly generalizable and require dividing the learned model into several specific classes of objects to avoid overfitting. We present a data-driven approach leveraging deep learning to encode a mapping function prior to mesh segmentation for multiple applications. Our network reproduces a neighborhood map using our knowledge of the \textsl{Shape Diameter Function} (SDF) method using similarities among vertex neighborhoods. Our approach is resolution-agnostic as we downsample the input meshes and query the full-resolution structure solely for neighborhood contributions. Using our predicted SDF values, we can inject the resulting structure into a graph-cut algorithm to generate an efficient and robust mesh segmentation while considerably reducing the required computation times.Comment: 9 pages, 13 figures, and 3 tables. Short paper and poster published and presented at SIGGRAPH 202

Model Simplification for Efficient Collision Detection in Robotics

Motion planning for industrial robots is a computationally intensive task due to the massive number of potential motions between any two configurations. Calculating all possibilities is generally not feasible. Instead, many motion planners sample a sub-set of the available space until a viable solution is found. Simplifying models to improve collision detection performance, a significant component of motion planning, results in faster and more capable motion planners. Several approaches for simplifying models to improve collision detection performance have been presented in the literature. However, many of them are sub-optimal for an industrial robotics application due to input model limitations, accuracy sacrifices, or the probability of increasing false negatives during collision queries. This thesis focuses on the development of model simplification approaches optimised for industrial robotics applications. Firstly, a new simplification approach, the Bounding Sphere Simplification (BSS), is presented that converts triangle-mesh inputs to a collection of spheres for efficient collision and distance queries. Additionally, BSS removes small features and generates an output model less prone to false negatives

PHYSICS-AWARE MODEL SIMPLIFICATION FOR INTERACTIVE VIRTUAL ENVIRONMENTS

Rigid body simulation is an integral part of Virtual Environments (VE) for autonomous planning, training, and design tasks. The underlying physics-based simulation of VE must be accurate and computationally fast enough for the intended application, which unfortunately are conflicting requirements. Two ways to perform fast and high fidelity physics-based simulation are: (1) model simplification, and (2) parallel computation. Model simplification can be used to allow simulation at an interactive rate while introducing an acceptable level of error. Currently, manual model simplification is the most common way of performing simulation speedup but it is time consuming. Hence, in order to reduce the development time of VEs, automated model simplification is needed. The dissertation presents an automated model simplification approach based on geometric reasoning, spatial decomposition, and temporal coherence. Geometric reasoning is used to develop an accessibility based algorithm for removing portions of geometric models that do not play any role in rigid body to rigid body interaction simulation. Removing such inaccessible portions of the interacting rigid body models has no influence on the simulation accuracy but reduces computation time significantly. Spatial decomposition is used to develop a clustering algorithm that reduces the number of fluid pressure computations resulting in significant speedup of rigid body and fluid interaction simulation. Temporal coherence algorithm reuses the computed force values from rigid body to fluid interaction based on the coherence of fluid surrounding the rigid body. The simulations are further sped up by performing computing on graphics processing unit (GPU). The dissertation also presents the issues pertaining to the development of parallel algorithms for rigid body simulations both on multi-core processors and GPU. The developed algorithms have enabled real-time, high fidelity, six degrees of freedom, and time domain simulation of unmanned sea surface vehicles (USSV) and can be used for autonomous motion planning, tele-operation, and learning from demonstration applications

Multi-scale modelling of effluent dispersion in the marine environment

This research aimed to investigate whether the unique numerical methods available within CFD model software Fluidity could progress the state– of–the–art in various aspects of modelling effluent dispersion within the marine environment. Fluidity contains a large library of models and numerical methods that enable modelling of flow processes at a wide range of scales. It has been proven to perform well when used for massively– parallel simulations (i.e. it scales well), and it has the un–common facility of unstructured mesh adaptivity, which has the prospect of significantly increasing the efficiency of CFD simulations when guided skillfully. This research also forms part of a longer–term project to create a coupled (or even single) model of effluent dispersion that represents influencing factors from a wide range of scales (from tidal currents down to turbulent eddies) entirely using CFD techniques. As such, one aspect of the research was to validate the numerical methods available within Fluidity for use in modelling effluent dispersion. To facilitate this validation, some of the model studies investigate aspects of effluent dispersion modelling from a hypothetical outfall site off the North–East coast of the United Kingdom. Studies were performed in a series of stages in which key aspects of effluent dispersion modelling were addressed. CFD models were created of near–field jet dispersion, tidal motion, and far–field plume dispersion. Idealised test cases were also performed to investigate the performance of advection–diffusion solver methods. At each stage the aim was to investigate the benefit of novel numerical modelling techniques and compare their accuracy and efficiency to existing methods. A set of near–field buoyant jet dispersion CFD models were created, one representing conditions associated with power, and combined power and desalination plants, and one representing conditions typically associated with desalination discharge. These CFD models utilised a mesh adaptivity algorithm to optimise mesh resolution during the course of the simulation. Model predictions were compared with published laboratory data and the predictions from validated integral models. An assessment was made of when CFD offers a benefit over other modelling options, and when it might be sufficient to use cheaper tools. There was also a discussion of the effectiveness of mesh adaptivity in increasing model efficiency, together with advice for how and when it is best to use mesh adaptivity when modelling buoyant jet dispersion. Model results showed that with modest parallel computing resources and expertise, high–resolution simulations of jet dynamics can be achieved with reasonable accuracy using CFD modelling. A model was created of tidal flow within the European continental shelf and results were compared to a large database of tide gauge measurements. This model took advantage of recently published methods for ocean model meshing and coastline resolution reduction. The purpose of this study was to confirm that these methods offered a benefit to model accuracy and efficient, and also that Fluidity could be used to accurately generate the tidal forcing boundary conditions for a far–field model of effluent dispersion at a hypothetical outfall site. The predictions of M2 tide amplitude in the vicinity of the outfall site had an average error of 10.1% compared with tide gauge measurements. The predictions of S2 tide amplitude in the vicinity of the outfall site were even closer to tide gauge measurements, with an average error of 3.7%. The speed of the model solve showed a vast improvement over a previous comparison model study, with 37 days of tidal motion being simulated in 15.2 hours (58.4 seconds of simulation for each second of solving), compared to the comparison simulation with a similar level of accuracy, which simulated 2 seconds of tidal motion for every second of solver time. A series of simplified test cases were run to assess a commonly–used advection–diffusion solution method from the library of those available within Fluidity. This work was intended to give general confidence that the numerical methods available within Fluidity are suitable for modelling coastal processes and so give confidence in later multi–scale results. The test cases chosen were relevant to coastal dispersion, including those testing tracer advection, diffusion, point sources and stratification. The method compared well with results published using world–leading free surface modelling software, Open TELEMAC. A model was created of the dispersion of neutrally–buoyant dissolved pollutant from a hypothetical outfall. The assumed effluent is typical of that released from a manufacturing plant. The aim of this modelling was to validate the use of Fluidity for modelling effluent dispersion within the coastal zone, and also investigate the benefit of using 2–d horizontal mesh adaptivity to optimise model mesh resolution during the course of the simulation. It was shown that the use of mesh adaptivity improved model efficiency, significantly lowering the effect of numerical diffusion. Finally, a short outline was given of a prospective strategy for producing a coupled–model of effluent dispersion, using as a basis the techniques developed within this thesis. The proposed coupled model of effluent dispersion would include a near–field jet model two–way (i.e. “fully– coupled”) to a far–field plume model. Tidal forcing would be provided by a one–way coupled tidal model. Fluidity is capable of modelling all of these processes and so third party coupling software would be unnecessary.Open Acces

Membrane locking in discrete shell theories

This work is concerned with the study of thin structures in Computational Mechanics. This field is particularly interesting, since together with traditional finite elements methods (FEM), the last years have seen the development of a new approach, called discrete differential geometry (DDG). The idea of FEM is to approximate smooth solutions using polynomials, providing error estimates that establish convergence in the limit of mesh refinement. The natural language of this field has been found in the formalism of functional analysis. On the contrary, DDG considers discrete entities, e.g., the mesh, as the only physical system to be studied and discrete theories are being formulated from first principles. In particular, DDG is concerned with the preservation of smooth properties that break down in the discrete setting with FEM. While the core of traditional FEM is based on function interpolation, usually in Hilbert spaces, discrete theories have an intrinsic physical interpretation, independently from the smooth solutions they converge to. This approach is related to flexible multibody dynamics and finite volumes. In this work, we focus on the phenomenon of membrane locking, which produces a severe artificial rigidity in discrete thin structures. In the case of FEM, locking arises from a poor choice of finite subspaces where to look for solutions, while in the DDG case, it arises from arbitrary definitions of discrete geometric quantities. In particular, we underline that a given mesh, or a given finite subspace, are not the physical system of interest, but a representation of it, out of infinitely many. In this work, we use this observation and combine tools from FEM and DDG, in order to build a novel discrete shell theory, free of membrane locking

Steganalytic Methods for 3D Objects

This PhD thesis provides new research results in the area of using 3D features for steganalysis. The research study presented in the thesis proposes new sets of 3D features, greatly extending the previously proposed features. The proposed steganlytic feature set includes features representing the vertex normal, curvature ratio, Gaussian curvature, the edge and vertex position of the 3D objects in the spherical coordinate system. Through a second contribution, this thesis presents a 3D wavelet multiresolution analysis-based steganalytic method. The proposed method extracts the 3D steganalytic features from meshes of different resolutions. The third contribution proposes a robustness and relevance-based feature selection method for solving the cover-source mismatch problem in 3D steganalysis. This method selects those 3D features that are robust to the variation of the cover source, while preserving the relevance of such features to the class label. All the proposed methods are applied for identifying stego-meshes produced by several steganographic algorithms