TetSplat: Real-time Rendering and Volume Clipping of Large Unstructured Tetrahedral Meshes

We present a novel approach to interactive visualization and exploration of large unstructured tetrahedral meshes. These massive 3D meshes are used in mission-critical CFD and structural mechanics simulations, and typically sample multiple field values on several millions of unstructured grid points. Our method relies on the pre-processing of the tetrahedral mesh to partition it into non-convex boundaries and internal fragments that are subsequently encoded into compressed multi-resolution data representations. These compact hierarchical data structures are then adaptively rendered and probed in real-time on a commodity PC. Our point-based rendering algorithm, which is inspired by QSplat, employs a simple but highly efficient splatting technique that guarantees interactive frame-rates regardless of the size of the input mesh and the available rendering hardware. It furthermore allows for real-time probing of the volumetric data-set through constructive solid geometry operations as well as interactive editing of color transfer functions for an arbitrary number of field values. Thus, the presented visualization technique allows end-users for the first time to interactively render and explore very large unstructured tetrahedral meshes on relatively inexpensive hardware

Meshless Animation Framework

This report details the implementation of a meshless animation framework for blending surfaces. The framework is meshless in the sense that only the control points are handled on the CPU, and the surface evaluation is delegated to the GPU using the tessellation shader steps. The framework handles regular grids and some forms of irregular grids. Different ways of handling the evaluation of the local surfaces are investigated. Directly evaluating them on the GPU or pre-evaluating them and only sampling the data on the GPU. Four different methods for pre-evaluation are presented, and the surface accuracy of each one is tested. The framework contains two methods for adaptively setting the level of detail on the GPU depending on position of the camera, using a view-based metric and a pixel-accurate rendering method. For both methods the pixel-accuracy and triangle size is tested and compared with static tessellation. Benchmarking results from the framework are presented. With and without animation, with different local surface types, and different resolution on the pre-evaluated data

Gap Processing for Adaptive Maximal Poisson-Disk Sampling

In this paper, we study the generation of maximal Poisson-disk sets with varying radii. First, we present a geometric analysis of gaps in such disk sets. This analysis is the basis for maximal and adaptive sampling in Euclidean space and on manifolds. Second, we propose efficient algorithms and data structures to detect gaps and update gaps when disks are inserted, deleted, moved, or have their radius changed. We build on the concepts of the regular triangulation and the power diagram. Third, we will show how our analysis can make a contribution to the state-of-the-art in surface remeshing.Comment: 16 pages. ACM Transactions on Graphics, 201

Detail Enhancing Denoising of Digitized 3D Models from a Mobile Scanning System

The acquisition process of digitizing a large-scale environment produces an enormous amount of raw geometry data. This data is corrupted by system noise, which leads to 3D surfaces that are not smooth and details that are distorted. Any scanning system has noise associate with the scanning hardware, both digital quantization errors and measurement inaccuracies, but a mobile scanning system has additional system noise introduced by the pose estimation of the hardware during data acquisition. The combined system noise generates data that is not handled well by existing noise reduction and smoothing techniques. This research is focused on enhancing the 3D models acquired by mobile scanning systems used to digitize large-scale environments. These digitization systems combine a variety of sensors – including laser range scanners, video cameras, and pose estimation hardware – on a mobile platform for the quick acquisition of 3D models of real world environments. The data acquired by such systems are extremely noisy, often with significant details being on the same order of magnitude as the system noise. By utilizing a unique 3D signal analysis tool, a denoising algorithm was developed that identifies regions of detail and enhances their geometry, while removing the effects of noise on the overall model. The developed algorithm can be useful for a variety of digitized 3D models, not just those involving mobile scanning systems. The challenges faced in this study were the automatic processing needs of the enhancement algorithm, and the need to fill a hole in the area of 3D model analysis in order to reduce the effect of system noise on the 3D models. In this context, our main contributions are the automation and integration of a data enhancement method not well known to the computer vision community, and the development of a novel 3D signal decomposition and analysis tool. The new technologies featured in this document are intuitive extensions of existing methods to new dimensionality and applications. The totality of the research has been applied towards detail enhancing denoising of scanned data from a mobile range scanning system, and results from both synthetic and real models are presented

Error-Bounded and Feature Preserving Surface Remeshing with Minimal Angle Improvement

The typical goal of surface remeshing consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application. In this paper, we design an algorithm to address all three optimization goals simultaneously. The user specifies desired bounds on approximation error {\delta}, minimal interior angle {\theta} and maximum mesh complexity N (number of vertices). Since such a desired mesh might not even exist, our optimization framework treats only the approximation error bound {\delta} as a hard constraint and the other two criteria as optimization goals. More specifically, we iteratively perform carefully prioritized local operators, whenever they do not violate the approximation error bound and improve the mesh otherwise. In this way our optimization framework greedily searches for the coarsest mesh with minimal interior angle above {\theta} and approximation error bounded by {\delta}. Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that our approach delivers high-quality meshes with implicitly preserved features and better balances between geometric fidelity, mesh complexity and element quality than the state-of-the-art.Comment: 14 pages, 20 figures. Submitted to IEEE Transactions on Visualization and Computer Graphic

Joint geometry and color point cloud denoising based on graph wavelets

A point cloud is an effective 3D geometrical presentation of data paired with different attributes such as transparency, normal and color of each point. The imperfect acquisition process of a 3D point cloud usually generates a significant amount of noise. Hence, point cloud denoising has received a lot of attention. Most of the existing techniques perform point cloud denoising based only on the geometry information of the neighbouring points; there are very few works considering the problem of denoising of color attributes of a point cloud, and taking advantage of the correlation between geometry and color. In this article, we introduce a novel non-iterative set-up for the denoising of point cloud based on spectral graph wavelet transform (SGW) that jointly exploits geometry and color to perform denoising of geometry and color attributes in graph spectral domain. The designed framework is based on the construction of joint geometry and color graph that compacts the energy of smooth graph signals in the low-frequency bands. The noise is then removed from the spectral graph wavelet coefficients by applying data-driven adaptive soft-thresholding. Extensive simulation results show that the proposed denoising technique significantly outperforms state-of-the-art methods using both subjective and objective quality metrics

Coarse-grained Multiresolution Structures for Mobile Exploration of Gigantic Surface Models

We discuss our experience in creating scalable systems for distributing and rendering gigantic 3D surfaces on web environments and common handheld devices. Our methods are based on compressed streamable coarse-grained multiresolution structures. By combining CPU and GPU compression technology with our multiresolution data representation, we are able to incrementally transfer, locally store and render with unprecedented performance extremely detailed 3D mesh models on WebGL-enabled browsers, as well as on hardware-constrained mobile devices