Advances in massive model visualization in the CYBERSAR project

We provide a survey of the major results obtained within the CYBERSAR project in the area of massive data visualization. Despite the impressive improvements in graphics and computational hardware performance, interactive visualization of massive models still remains a challenging problem. To address this problem, we developed methods that exploit the programmability of latest generation graphics hardware, and combine coarse-grained multiresolution models, chunk-based data management with compression, incremental view-dependent level-of-detail selection, and visibility culling. The models that can be interactively rendered with our methods range from multi-gigabyte-sized datasets for general 3D meshes or scalar volumes, to terabyte-sized datasets in the restricted 2.5D case of digital terrain models. Such a performance enables novel ways of exploring massive datasets. In particular, we have demonstrated the capability of driving innovative light field displays able of giving multiple freely moving naked-eye viewers the illusion of seeing and manipulating massive 3D objects with continuous viewer-independent parallax.233-23

CYBERSAR

The project aims at setting up an advanced cyberinfrastructure based on dedicated optical networks to support collaborative research application. The aim of Cybersar computational infrastructure is to support innovative computational applications by using leading edge hardware and technological solutions and to provide an experimental platform for research on the enabling technologies that will power next generation cyberinfrastructures. In particular, in the Visual Computing Group, we study techniques for processing and rendering very large scale 3D datasets on innovative large scale displays.Completato€ 1.291.50

Planet-Sized Batched Dynamic Adaptive Meshes (P-BDAM)

This paper describes an efficient technique for out-of-core management and interactive rendering of planet sized textured terrain surfaces. The technique, called planet-sized batched dynamic adaptive meshes (P-BDAM), extends the BDAM approach by using as basic primitive a general triangulation of points on a displaced triangle. The proposed framework introduces several advances with respect to the state of the art: thanks to a batched host-to-graphics communication model, we outperform current adaptive tessellation solutions in terms of rendering speed; we guarantee overall geometric continuity, exploiting programmable graphics hardware to cope with the accuracy issues introduced by single precision floating points; we exploit a compressed out of core representation and speculative prefetching for hiding disk latency during rendering of out-of-core data; we efficiently construct high quality simplified representations with a novel distributed out of core simplification algorithm working on a standard PC network.147-15

Survey of semi-regular multiresolution models for interactive terrain rendering

Rendering high quality digital terrains at interactive rates requires carefully crafted algorithms and data structures able to balance the competing requirements of realism and frame rates, while taking into account the memory and speed limitations of the underlying graphics platform. In this survey, we analyze multiresolution approaches that exploit a certain semi-regularity of the data. These approaches have produced some of the most efficient systems to date. After providing a short background and motivation for the methods, we focus on illustrating models based on tiled blocks and nested regular grids, quadtrees and triangle bin-trees triangulations, as well as cluster-based approaches. We then discuss LOD error metrics and system-level data management aspects of interactive terrain visualization, including dynamic scene management, out-of-core data organization and compression, as well as numerical accurac

LOCALIS: Locally-adaptive Line Simplification for GPU-based Geographic Vector Data Visualization

Visualization of large vector line data is a core task in geographic and cartographic systems. Vector maps are often displayed at different cartographic generalization levels, traditionally by using several discrete levels-of-detail (LODs). This limits the generalization levels to a fixed and predefined set of LODs, and generally does not support smooth LOD transitions. However, fast GPUs and novel line rendering techniques can be exploited to integrate dynamic vector map LOD management into GPU-based algorithms for locally-adaptive line simplification and real-time rendering. We propose a new technique that interactively visualizes large line vector datasets at variable LODs. It is based on the Douglas-Peucker line simplification principle, generating an exhaustive set of line segments whose specific subsets represent the lines at any variable LOD. At run time, an appropriate and view-dependent error metric supports screen-space adaptive LOD levels and the display of the correct subset of line segments accordingly. Our implementation shows that we can simplify and display large line datasets interactively. We can successfully apply line style patterns, dynamic LOD selection lenses, and anti-aliasing techniques to our line rendering

GPU-based Streaming for Parallel Level of Detail on Massive Model Rendering

Rendering massive 3D models in real-time has long been recognized as a very challenging problem because of the limited computational power and memory space available in a workstation. Most existing rendering techniques, especially level of detail (LOD) processing, have suffered from their sequential execution natures, and does not scale well with the size of the models. We present a GPU-based progressive mesh simplification approach which enables the interactive rendering of large 3D models with hundreds of millions of triangles. Our work contributes to the massive rendering research in two ways. First, we develop a novel data structure to represent the progressive LOD mesh, and design a parallel mesh simplification algorithm towards GPU architecture. Second, we propose a GPU-based streaming approach which adopt a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Our results show that the parallel mesh simplification algorithm and GPU-based streaming approach significantly improve the overall rendering performance

Time-critical multiresolution rendering of large complex models

Very large and geometrically complex scenes, exceeding millions of polygons and hundreds of objects, arise naturally in many areas of interactive computer graphics. Time-critical rendering of such scenes requires the ability to trade visual quality with speed. Previous work has shown that this can be done by representing individual scene components as multiresolution triangle meshes, and performing at each frame a convex constrained optimization to choose the mesh resolutions that maximize image quality while meeting timing constraints. In this paper we demonstrate that the nonlinear optimization problem with linear constraints associated to a large class of quality estimation heuristics is efficiently solved using an active-set strategy. By exploiting the problem structure, Lagrange multipliers estimates and equality constrained problem solutions are computed in linear time. Results show that our algorithms and data structures provide low memory overhead, smooth level-of-detail control, and guarantee, within acceptable limits, a uniform, bounded frame rate even for widely changing viewing conditions. Implementation details are presented along with the results of tests for memory needs, algorithm timing, and efficacy.785-803Pubblicat

Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. An accurate inventory map is a prerequisite for the analysis of landslide susceptibility, hazard, and risk. Field survey, optical remote sensing, and synthetic aperture radar techniques are traditional techniques for landslide detection in tropical regions. However, such techniques are time consuming and costly. In addition, the dense vegetation of tropical forests complicates the generation of an accurate landslide inventory map for these regions. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) has been used to generate accurate landslide maps. This study proposes the use of recurrent neural networks (RNN) and multi-layer perceptron neural networks (MLP-NN) in landscape detection. These efficient neural architectures require little or no prior knowledge compared with traditional classification methods. The proposed methods were tested in the Cameron Highlands, Malaysia. Segmentation parameters and feature selection were respectively optimized using a supervised approach and correlation-based feature selection. The hyper-parameters of network architecture were defined based on a systematic grid search. The accuracies of the RNN and MLP-NN models in the analysis area were 83.33% and 78.38%, respectively. The accuracies of the RNN and MLP-NN models in the test area were 81.11%, and 74.56%, respectively. These results indicated that the proposed models with optimized hyper-parameters produced the most accurate classification results. LiDAR-derived data, orthophotos, and textural features significantly affected the classification results. Therefore, the results indicated that the proposed methods have the potential to produce accurate and appropriate landslide inventory in tropical regions such as Malaysia

Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images

Stereo matching is an open problem in Computer Vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors. We propose a solution to the problem of how deep (coarse) should the stereo measures start, trading between error minimization and time consumption, by starting stereo calculation at varying resolution levels, for each pixel, according to fuzzy decisions. Our heuristic enhances the overall execution time since it only employs deeper resolution levels when strictly necessary. It also reduces errors because it measures similarity between windows with enough details. We also compare our algorithm with a very fast multi-resolution approach, and one based on fuzzy logic. Our algorithm performs faster and/or better than all those approaches, becoming, thus, a good candidate for robotic vision applications. We also discuss the system architecture that efficiently implements our solution