Fabio Marton

BDAM — Batched Dynamic Adaptive Meshes for High Performance Terrain Visualization

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.

Adaptive tetrapuzzles: efficient out-of-core construction and visualization of gigantic multiresolution polygonal models

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.

Planet-sized batched dynamic adaptive meshes (P-BDAM)

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.

A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets

We present an adaptive out-of-core technique for rendering massive scalar volumes employing single-pass GPU ray casting. The method is based on the decomposition of a volumetric dataset into small cubical bricks, which are then organized into an octree structure maintained out-of-core. The octree contains the original data at the leaves, and a filtered representation of children at inner nodes. At runtime an adaptive loader, executing on the CPU, updates a view and transfer function-dependent working set of bricks maintained on GPU memory by asynchronously fetching data from the out-of-core octree representation. At each frame, a compact indexing structure, which spatially organizes the current working set into an octree hierarchy, is encoded in a small texture. This data structure is then exploited by an efficient stackless ray casting algorithm, which computes the volume rendering integral by visiting non-empty bricks in front-to-back order and adapting sampling density to brick resolution. Block visibility information is fed back to the loader to avoid refinement and data loading of occluded zones. The resulting method is able to interactively explore multi-gigavoxel datasets on a desktop PC.

Far voxels: a multiresolution framework for interactive rendering of huge complex 3D models on commodity graphics platforms

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.

C-BDAM - Compressed Batched Dynamic Adaptive Meshes for Terrain Rendering

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.

Batched multi triangulation

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.

Layered point clouds

We present a simple point-based multiresolution structure for interactive visualization of very large point sampled models on consumer graphics platforms. The structure is based on a hierarchy of precomputed object-space point clouds. At rendering time, the clouds are combined coarse-to-fine with a top-down structure traversal to locally adapt sample densities according to the projected size in the image. Since each cloud is made of a few thousands of samples, the multiresolution extraction cost is amortized over many graphics primitives, and host-to-graphics communication effectively exploits on-board caching and object based rendering APIs. The progressive block based refinement nature of the rendering traversal is well suited to hiding out-of-core data access latency, and lends itself well to incorporate backface, view frustum, and occlusion culling, as well as compression and viewdependent progressive transmission. The resulting system allows rendering of complex models at high frame rates (over 60M splat/second), supports network streaming, and is fundamentally simple to implement.

Ray-Casted BlockMaps for Large Urban Models Visualization

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.

Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization

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.