Abstract We introduce a novel approach for assisting users in exploring 2D data representations with an interactive lens. Focus‐and‐context exploration is supported by translating user actions to the joint adjustments in camera and lens parameters that ensure a good placement and sizing of the lens within the view. This general approach, implemented using standard device mappings, overcomes the limitations of current solutions, which force users to continuously switch from lens positioning and scaling to view panning and zooming. Navigation is further assisted by exploiting data annotations. In addition to traditional visual markups and information links, we associate to each annotation a lens configuration that highlights the region of interest. During interaction, an assisting controller determines the next best lens in the database based on the current view and lens parameters and the navigation history. Then, the controller interactively guides the user's lens towards the selected target and displays its annotation markup. As only one annotation markup is displayed at a time, clutter is reduced. Moreover, in addition to guidance, the navigation can also be automated to create a tour through the data. While our methods are generally applicable to general 2D visualization, we have implemented them for the exploration of stratigraphic relightable models. The capabilities of our approach are demonstrated in cultural heritage use cases. A user study has been performed in order to validate our approach.

Abstract We introduce a novel flexible approach to spatiotemporal exploration of rectilinear scalar volumes. Our out‐of‐core representation, based on per‐frame levels of hierarchically tiled non‐redundant 3D grids, efficiently supports spatiotemporal random access and streaming to the GPU in compressed formats. A novel low‐bitrate codec able to store into fixed‐size pages a variable‐rate approximation based on sparse coding with learned dictionaries is exploited to meet stringent bandwidth constraint during time‐critical operations, while a near‐lossless representation is employed to support high‐quality static frame rendering. A flexible high‐speed GPU decoder and raycasting framework mixes and matches GPU kernels performing parallel object‐space and image‐space operations for seamless support, on fat and thin clients, of different exploration use cases, including animation and temporal browsing, dynamic exploration of single frames, and high‐quality snapshots generated from near‐lossless data. The quality and performance of our approach are demonstrated on large data sets with thousands of multi‐billion‐voxel frames.

course Share on Mobile graphics Authors: Marco Agus KAUST & CRS4 KAUST & CRS4View Profile , Enrico Gobbetti CRS4 CRS4View Profile , Fabio Marton CRS4 CRS4View Profile , Giovanni Pintore CRS4 CRS4View Profile , Pere-Pau Vázquez UPC UPCView Profile Authors Info & Claims SA '17: SIGGRAPH Asia 2017 CoursesNovember 2017 Article No.: 12Pages 1–259https://doi.org/10.1145/3134472.3134483Published:27 November 2017Publication History 1citation239DownloadsMetricsTotal Citations1Total Downloads239Last 12 Months19Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Voxelized representations of complex 3D scenes are widely used nowadays to accelerate visibility queries in many GPU rendering techniques. Since GPU memory is limited, it is important that these data structures can be kept within a strict memory budget. Recently, directed acyclic graphs (DAGs) have been successfully introduced to compress sparse voxel octrees (SVOs), but they are limited to sharing identical regions of space. In this paper, we show that a more efficient lossless compression of geometry can be achieved, while keeping the same visibility-query performance, by merging subtrees that are identical through a similarity transform, and by exploiting the skewed distribution of references to shared nodes to store child pointers using a variabile bit-rate encoding. We also describe how, by selecting plane reflections along the main grid directions as symmetry transforms, we can construct highly compressed GPU-friendly structures using a fully out-of-core method. Our results demonstrate that state-of-the-art compression and real-time tracing performance can be achieved on high-resolution voxelized representations of real-world scenes of very different characteristics, including large CAD models, 3D scans, and typical gaming models, leading, for instance, to real-time GPU in-core visualization with shading and shadows of the full Boeing 777 at sub-millimetric precision.

Abstract We introduce a novel approach for letting casual viewers explore detailed 3D models integrated with structured spatially associated descriptive information organized in a graph. Each node associates a subset of the 3D surface seen from a particular viewpoint to the related descriptive annotation, together with its author‐defined importance. Graph edges describe, instead, the strength of the dependency relation between information nodes, allowing content authors to describe the preferred order of presentation of information. At run‐time, users navigate inside the 3D scene using a camera controller, while adaptively receiving unobtrusive guidance towards interesting viewpoints and history‐ and location‐dependent suggestions on important information, which is adaptively presented using 2D overlays displayed over the 3D scene. The capabilities of our approach are demonstrated in a real‐world cultural heritage application involving the public presentation of sculptural complex on a large projection‐based display. A user study has been performed in order to validate our approach.

Abstract Great advancements in commodity graphics hardware have favoured graphics processing unit (GPU)‐based volume rendering as the main adopted solution for interactive exploration of rectilinear scalar volumes on commodity platforms. Nevertheless, long data transfer times and GPU memory size limitations are often the main limiting factors, especially for massive, time‐varying or multi‐volume visualization, as well as for networked visualization on the emerging mobile devices. To address this issue, a variety of level‐of‐detail (LOD) data representations and compression techniques have been introduced. In order to improve capabilities and performance over the entire storage, distribution and rendering pipeline, the encoding/decoding process is typically highly asymmetric, and systems should ideally compress at data production time and decompress on demand at rendering time. Compression and LOD pre‐computation does not have to adhere to real‐time constraints and can be performed off‐line for high‐quality results. In contrast, adaptive real‐time rendering from compressed representations requires fast, transient and spatially independent decompression. In this report, we review the existing compressed GPU volume rendering approaches, covering sampling grid layouts, compact representation models, compression techniques, GPU rendering architectures and fast decoding techniques.

Abstract We present a novel multiresolution compression‐domain GPU volume rendering architecture designed for interactive local and networked exploration of rectilinear scalar volumes on commodity platforms. In our approach, the volume is decomposed into a multiresolution hierarchy of bricks. Each brick is further subdivided into smaller blocks, which are compactly described by sparse linear combinations of prototype blocks stored in an overcomplete dictionary. The dictionary is learned, using limited computational and memory resources, by applying the K‐SVD algorithm to a re‐weighted non‐uniformly sampled subset of the input volume, harnessing the recently introduced method of coresets. The result is a scalable high quality coding scheme, which allows very large volumes to be compressed off‐line and then decompressed on‐demand during real‐time GPU‐accelerated rendering. Volumetric information can be maintained in compressed format through all the rendering pipeline. In order to efficiently support high quality filtering and shading, a specialized real‐time renderer closely coordinates decompression with rendering, combining at each frame images produced by raycasting selectively decompressed portions of the current view‐ and transfer‐function‐dependent working set. The quality and performance of our approach is demonstrated on massive static and time‐varying datasets.

Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.

This course instructs students in the software and hardware strategies needed for real-time visualization and interaction with massive models. Seven international researchers and practitioners are the instructors. The general form of the course will be lecture with live demos.

Abstract We present a GPU accelerated volume ray casting system interactively driving a multi‐user light field display. The display, driven by a single programmable GPU, is based on a specially arranged array of projectors and a holographic screen and provides full horizontal parallax. The characteristics of the display are exploited to develop a specialized volume rendering technique able to provide multiple freely moving naked‐eye viewers the illusion of seeing and manipulating virtual volumetric objects floating in the display workspace. In our approach, a GPU ray‐caster follows rays generated by a multiple‐center‐of‐projection technique while sampling pre‐filtered versions of the dataset at resolutions that match the varying spatial accuracy of the display. The method achieves interactive performance and provides rapid visual understanding of complex volumetric data sets even when using depth oblivious compositing techniques.

Abstract We introduce a GPU‐friendly technique that efficiently exploits the highly structured nature of urban environments to ensure rendering quality and interactive performance of city exploration tasks. Central to our approach is a novel discrete representation, called BlockMap, for the efficient encoding and rendering of a small set of textured buildings far from the viewer. A BlockMap compactly represents a set of textured vertical prisms with a bounded on‐screen footprint. BlockMaps are stored into small fixed size texture chunks and efficiently rendered through GPU raycasting. Blockmaps can be seamlessly integrated into hierarchical data structures for interactive rendering of large textured urban models. We illustrate an efficient output‐sensitive framework in which a visibility‐aware traversal of the hierarchy renders components close to the viewer with textured polygons and employs BlockMaps for far away geometry. Our approach provides a bounded size far distance representation of cities, naturally scales with the improving shader technology, and outperforms current state of the art approaches. Its efficiency and generality is demonstrated with the interactive exploration of a large textured model of the city of Paris on a commodity graphics platform.

Abstract We describe a compressed multiresolution representation for supporting interactive rendering of very large planar and spherical terrain surfaces. The technique, called Compressed Batched Dynamic Adaptive Meshes (C‐BDAM), is an extension of the BDAM and P‐BDAM chunked level‐of‐detail hierarchy. In the C‐BDAM approach, all patches share the same regular triangulation connectivity and incrementally encode their vertex attributes using a quantized representation of the difference with respect to values predicted from the coarser level. The structure provides a number of benefits: simplicity of data structures, overall geometric continuity for planar and spherical domains, support for variable resolution input data, management of multiple vertex attributes, efficient compression and fast construction times, ability to support maximum‐error metrics, real‐time decompression and shaded rendering with configurable variable level‐of‐detail extraction, and runtime detail synthesis. The efficiency of the approach and the achieved compression rates are demonstrated on a number of test cases, including the interactive visualization of a 29 gigasample reconstruction of the whole planet Earth created from high resolution SRTM data. Categories and Subject Descriptors (according toACMCCS): I.3.3 [Computer Graphics]: Picture and Image Generation; I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism.

The multi triangulation framework (MT) is a very general approach for managing adaptive resolution in triangle meshes. The key idea is arranging mesh fragments at different resolution in a directed acyclic graph (DAG) which encodes the dependencies between fragments, thereby encompassing a wide class of multiresolution approaches that use hierarchies or DAGs with predefined topology. On current architectures, the classic MT is however unfit for real-time rendering, since DAG traversal costs vastly dominate raw rendering costs. In this paper, we redesign the MT framework in a GPU friendly fashion, moving its granularity from triangles to precomputed optimized triangle patches. The patches can be conveniently tri-stripped and stored in secondary memory to be loaded on demand, ready to be sent to the GPU using preferential paths. In this manner, central memory only contains the DAG structure and CPU workload becomes negligible. The major contributions of this work are: a new out-of-core multiresolution framework, that, just like the MT, encompasses a wide class of multiresolution structures; a robust and elegant way to build a well conditioned MT DAG by introducing the concept of V-partitions, that can encompass various state of the art multiresolution algorithms; an efficient multithreaded rendering engine and a general subsystem for the external memory processing and simplification of huge meshes.

We present an efficient approach for end-to-end out-of-core construction and interactive inspection of very large arbitrary surface models. The method tightly integrates visibility culling and out-of-core data management with a level-of-detail framework. At preprocessing time, we generate a coarse volume hierarchy by binary space partitioning the input triangle soup. Leaf nodes partition the original data into chunks of a fixed maximum number of triangles, while inner nodes are discretized into a fixed number of cubical voxels. Each voxel contains a compact direction dependent approximation of the appearance of the associated volumetric subpart of the model when viewed from a distance. The approximation is constructed by a visibility aware algorithm that fits parametric shaders to samples obtained by casting rays against the full resolution dataset. At rendering time, the volumetric structure, maintained off-core, is refined and rendered in front-to-back order, exploiting vertex programs for GPU evaluation of view-dependent voxel representations, hardware occlusion queries for culling occluded subtrees, and asynchronous I/O for detecting and avoiding data access latencies. Since the granularity of the multiresolution structure is coarse, data management, traversal and occlusion culling cost is amortized over many graphics primitives. The efficiency and generality of the approach is demonstrated with the interactive rendering of extremely complex heterogeneous surface models on current commodity graphics platforms.

We describe an efficient technique for out-of-core construction and accurate view-dependent visualization of very large surface models. The method uses a regular conformal hierarchy of tetrahedra to spatially partition the model. Each tetrahedral cell contains a precomputed simplified version of the original model, represented using cache coherent indexed strips for fast rendering. The representation is constructed during a fine-to-coarse simplification of the surface contained in diamonds (sets of tetrahedral cells sharing their longest edge). The construction preprocess operates out-of-core and parallelizes nicely. Appropriate boundary constraints are introduced in the simplification to ensure that all conforming selective subdivisions of the tetrahedron hierarchy lead to correctly matching surface patches. For each frame at runtime, the hierarchy is traversed coarse-to-fine to select diamonds of the appropriate resolution given the view parameters. The resulting system can interatively render high quality views of out-of-core models of hundreds of millions of triangles at over 40Hz (or 70M triangles/s) on current commodity graphics platforms.

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.

Abstract This paper describes an efficient technique for out‐of‐core rendering and management of large textured terrainsurfaces. 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 smallpatches are TINs and are constructed and optimized off‐line with high quality simplification and tristrippingalgorithms. Hierarchical view frustum culling and view‐dependent texture and geometry refinement is performedat 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 terraindatasets. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Picture and Image Generation;I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism.