Author
Enrico Gobbetti
Other affiliations: Istituto di Scienza e Tecnologie dell'Informazione, Leonardo, Polaris Industries ...read more
Bio: Enrico Gobbetti is an academic researcher from Center for Advanced Studies Research and Development in Sardinia. The author has contributed to research in topics: Rendering (computer graphics) & Visualization. The author has an hindex of 40, co-authored 240 publications receiving 5467 citations. Previous affiliations of Enrico Gobbetti include Istituto di Scienza e Tecnologie dell'Informazione & Leonardo.
Papers published on a yearly basis
Papers
More filters
TL;DR: This paper describes an efficient technique for out‐of‐core rendering and management of large textured terrainsurfaces based on a paired tree structure that fully harnesses the power of current graphics hardware.
Abstract: This paper describes an efficient technique for out-of-core rendering and management of large textured terrain surfaces. The technique, called Batched Dynamic Adaptive Meshes (BDAM) , is based on a paired tree structure: a tiled quadtree for texture data and a pair of bintrees of small triangular patches for the geometry. These small patches are TINs and are constructed and optimized off-line with high quality simplification and tristripping algorithms. Hierarchical view frustum culling and view-dependent texture and geometry refinement is performed at each frame through a stateless traversal algorithm. Thanks to the batched CPU/GPU communication model, the proposed technique is not processor intensive and fully harnesses the power of current graphics hardware. Both preprocessing and rendering exploit out-of-core techniques to be fully scalable and to manage large terrain datasets.
226 citations
TL;DR: This survey analyzes multiresolution approaches that exploit a certain semi-regularity of the data, including dynamic scene management, out-of-core data organization and compression, as well as numerical accuracy.
Abstract: 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 accuracy.
195 citations
TL;DR: A robust approach for reconstructing the main architectural structure of complex indoor environments given a set of cluttered 3D input range scans, using an efficient occlusion-aware process and automatically extracts the individual rooms that compose the environment by applying a diffusion process on the space partitioning induced by the candidate walls.
157 citations
22 Oct 2003
TL;DR: The proposed framework introduces several advances with respect to the state of the art: thanks to a batched host-to-graphics communication model, it outperform current adaptive tessellation solutions in terms of rendering speed and guarantees overall geometric continuity.
Abstract: We describe 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.
156 citations
Cited by
More filters
TL;DR: In this paper, the authors offer a new book that enPDFd the perception of the visual world to read, which they call "Let's Read". But they do not discuss how to read it.
Abstract: Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.
2,250 citations
TL;DR: It is shown that for the cases studied here, the relatively simple Min?min heuristic performs well in comparison to the other techniques, and one even basis for comparison and insights into circumstances where one technique will out-perform another.
1,757 citations
TL;DR: The authors' approach uses a genetic algorithm to select subsets of attributes or features to represent the patterns to be classified, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.
Abstract: Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.
1,465 citations
TL;DR: The main focus will be on the different approaches to perform robust optimization in practice including the methods of mathematical programming, deterministic nonlinear optimization, and direct search methods such as stochastic approximation and evolutionary computation.
1,435 citations