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

Study of Subjective and Objective Quality Evaluation of 3D Point Cloud Data by the JPEG Committee

The SC29/WG1 (JPEG) Committee within ISO/IEC is currently working on developing standards for the storage, compression and transmission of 3D point cloud information. To support the creation of these standards, the committee has created a database of 3D point clouds representing various quality levels and use-cases and examined a range of 2D and 3D objective quality measures. The examined quality measures are correlated with subjective judgments for a number of compression levels. In this paper we describe the database created, tests performed and key observations on the problems of 3D point cloud quality assessment

Rapid Visualization of Large Point-Based Surfaces

International audiencePoint-Based Surfaces can be directly generated by 3D scanners and avoid the generation and storage of an explicit topology for a sampled geometry, which saves time and storage space for very dense and large objects, such as scanned statues and other archaeological artefacts [Duguet 2004]. We propose a fast processing pipeline of large point-based surfaces for real-time, appearance preserving, polygonal rendering. Our goal is to reduce the time needed between a point set made of hundred of millions samples and a high resolution visualization taking benefit of modern graphics hardware, tuned for normal mapping of polygons. Our approach starts by an out-of-core generation of a coarse local triangulation of the original model. The resulting coarse mesh is enriched by applying a set of maps which capture the high frequency features of the original data set. We choose as an example the normal component of samples for these maps, since normal maps provide efficiently an accurate local illumination. But our approach is also suitable for other point attributes such as color or position (displacement map). These maps come also from an out-of-core process, using the complete input data in a streaming process. Sampling issues of the maps are addressed using an efficient diffusion algorithm in 2D. Our main contribution is to directly handle such large unorganized point clouds through this two pass algorithm, without the time-consuming meshing or parameterization step, required by current state-of-the-art high resolution visualization methods. One of the main advantages is to express most of the fine features present in the original large point clouds as textures in the huge texture memory usually provided by graphics devices, using only a lazy local parameterization. Our technique comes as a complementary tool to high-quality, but costly, out-of-core visualization systems. Direct applications are: interactive preview at high screen resolution of very detailed scanned objects such as scanned statues, inclusion of large point clouds in usual polygonal 3D engines and 3D databases browsing

Perceptual evaluation of point-based object representation

Researchers have proposed several methods of representing three-dimensional objects in computer memory along with different methods of displaying these objects on two-dimensional computer displays. Using a polygon-based representation that linearly interpolates between a set of sample points is by far the most popular way of representing objects today, mostly because of its ability to display an object on a computer screen at a fast and consistent rate. However, as graphics hardware increases in speed, polygon-based models prove to be increasingly inefficient. Point-based representations have been proposed that consist of densely sampling some surface and using solely the samples to portray the object on a computer display. This research proposes a similar approach to point-based representation while making use of current graphics hardware to compare the benefits and drawbacks of using point-based representations over polygon-based representations in interactive environments. An experiment has been conducted with human subjects to gather perceptual data about each representation method. The results from this experiment are presented and analyzed

Visual Data Representation using Context-Aware Samples

The rapid growth in the complexity of geometry models has necessisated revision of several conventional techniques in computer graphics. At the heart of this trend is the representation of geometry with locally constant approximations using independent sample primitives. This generally leads to a higher sampling rate and thus a high cost of representation, transmission, and rendering. We advocate an alternate approach involving context-aware samples that capture the local variation of the geometry. We detail two approaches; one, based on differential geometry and the other based on statistics. Our differential-geometry-based approach captures the context of the local geometry using an estimation of the local Taylor's series expansion. We render such samples using programmable Graphics Processing Unit (GPU) by fast approximation of the geometry in the screen space. The benefits of this representation can also be seen in other applications such as simulation of light transport. In our statistics-based approach we capture the context of the local geometry using Principal Component Analysis (PCA). This allows us to achieve hierarchical detail by modeling the geometry in a non-deterministic fashion as a hierarchical probability distribution. We approximate the geometry and its attributes using quasi-random sampling. Our results show a significant rendering speedup and savings in the geometric bandwidth when compared to current approaches