Existing intrinsic triangulation frameworks represent powerful tools for geometry processing; however, they all require the extraction of the common subdivision between extrinsic and intrinsic triangulations for visualization and optimized data transfer. We describe an efficient and effective algorithm for directly rendering intrinsic triangulations that avoids extracting common subdivisions. Our strategy is to use GPU shaders to render the intrinsic triangulation while rasterizing extrinsic triangles. We rely on a point-location algorithm supported by a compact data structure, which requires only two values per extrinsic triangle to represent the correspondence between extrinsic and intrinsic triangulations. This data structure is easier to maintain than previous proposals while supporting all the standard topological operations for improving the intrinsic mesh quality, such as edge flips, triangle refinements, and vertex displacements. Computational experiments show that the proposed data structure is numerically robust and can process nearly degenerate triangulations. We also propose a meshless strategy to accurately transfer data from intrinsic to extrinsic triangulations without relying on the extraction of common subdivisions.

Building Information Modeling (BIM) employs data-rich 3D CAD models for large-scale facility design, construction, and operation. These complex datasets contain a large amount and variety of information, ranging from design specifications to real-time sensor data. They are used by architects and engineers for various analysis and simulations throughout a facility's life cycle. Many techniques from different visualization fields could be used to analyze these data. However, the BIM domain still remains largely unexplored by the visualization community. The goal of this article is to encourage visualization researchers to increase their involvement with BIM. To this end, we present the results of a systematic review of visualization in current BIM practice. We use a novel taxonomy to identify main application areas and analyze commonly employed techniques. From this domain characterization, we highlight future research opportunities brought forth by the unique features of BIM. For instance, exploring the synergies between scientific and information visualization to integrate spatial and non-spatial data. We hope this article raises awareness to interesting new challenges the BIM domain brings to the visualization community.

Efficient rendering of massive industrial-plant CAD models is a long-time challenge even when using high-end computers. This problem has been aggravated with the popularization of web-based applications and lightweight devices (e.g., tablets and smartphones) in engineering daily basis activities. One promising solution consists in pushing rendering tasks to the cloud. However, the existing cloud rendering platforms are not able to render CAD models efficiently. In this work, we aim to fill the gap between industrial-plant models and cloud rendering by proposing a hybrid cloud rendering method. In our approach, we define two sets of objects: one for rendering on client-side, and the other for rendering on server-side. The client produces the final image combining local and remote images. As a consequence, our cloud rendering system provides better image quality and more robustness to network fluctuations. A key component of such a solution is the choice of objects to be rendered on the client. We propose a metaheuristic-based method to select such objects. Our solution privileges larger objects with a uniform spatial distribution. This way, the user can still coherently navigate even when the server is not responding. Our results show that our approach enables thin devices to interactively visualize massive CAD models with good image quality, even when the networking is not performing well.

Building Information Modeling (BIM) provides an integrated 3D environment to manage large-scale engineering projects. The Architecture, Engineering and Construction (AEC) industry explores 4D visualizations over these datasets for virtual construction planning. However, existing solutions lack adequate visual mechanisms to inspect the underlying schedule and make inconsistencies readily apparent. The goal of this paper is to apply best practices of information visualization to improve 4D analysis of construction plans. We first present a review of previous work that identifies common use cases and limitations. We then consulted with AEC professionals to specify the main design requirements for such applications. These guided the development of CasCADe, a novel 4D visualization system where task sequencing and spatio-temporal simultaneity are immediately apparent. This unique framework enables the combination of diverse analytical features to create an information-rich analysis environment. We also describe how engineering collaborators used CasCADe to review the real-world construction plans of an Oil & Gas process plant. The system made evident schedule uncertainties, identified work-space conflicts and helped analyze other constructability issues. The results and contributions of this paper suggest new avenues for future research in information visualization for the AEC industry.

Fast corner-detector algorithms are important for achieving real time in different computer vision applications. In this paper, we present new algo-rithm implementations for corner detection that make use of graphics pro-cessing units (GPU) provided by commodity hardware. The programmable capabilities of modern GPUs allow speeding up counterpart CPU algorithms. In the case of corner-detector algorithms, most steps are easily translated from CPU to GPU. However, there are challenges for mapping the feature selection step to the GPU parallel computational model. This paper presents a template for implementing corner-detector algorithms that run entirely on GPU, resulting in significant speed-ups. The proposed template is used to implement the KLT corner detector and the Harris corner detector, and nu-merical results are presented to demonstrate the algorithms efficiency. 1

Adaptively Sampled Distance Fields (ADFs) are volumetric shape representations that support a broad range of applications in the areas of computer graphics, computer vision and physics. ADFs are especially beneficial for representing shapes with features at very diverse scales. In this paper, we propose a strategy to represent and reconstruct ADFs on modern graphics hardware (GPUs). We employ a 3D hashing scheme to store the underlying data structure and try to balance the tradeoff between memory requirements and reconstruction efficiency. To render ADFs on GPU, we use a general-purpose ray-casting technique based on sphere tracing, which guarantees the reconstruction of fine details. We also present a way to overcome the Cl discontinuities inherent to ADFs and efficiently reconstruct smooth surface normals across cell boundaries. The effectiveness of our proposal is demonstrated for isosurface rendering and morphing.

Three-dimensional visualization is a valuable technique in the oil industry. One of its important applications is for inspecting black oil reservoir models, allowing engineers to design and analyze different model configurations, focusing on maximum production and minimum cost. These models are huge structures, with complex well arrangements, that require fly-through navigation for accurate analysis. The common approach of direct camera control is difficult for inexperienced users and results in inappropriate camera motions and collisions with the environment, which can easily lead to user distraction. Moreover, investments are being made on immersive caves, and environment collisions in this case are very uncomfortable. It becomes necessary to provide an assisted navigation, in which the user guides the camera more easily, without restricting the environment exploration. Also, it is interesting to provide a fully automatic navigation mode, in which the user selects a target and the system computes a smooth, collision-free path throughout the environment.In this paper, we present a specific solution for navigation in models used in numerical simulations of black oil reservoirs. Our approach is based on probabilistic roadmaps. A global roadmap of the environment is pre-computed using a randomized motion-planning algorithm. At runtime, graph searching is performed using the A* algorithm to compute collision-free paths. We propose a new roadmap-construction algorithm based on a hierarchical environment-sampling strategy, along with a topology-based coverage estimate. For multi-layered reservoir models, we present a new heuristic function for the A* algorithm that greatly increases graph searching performance. We also present new approaches to use the pre-computed roadmap for providing both assisted and automatic navigation at runtime.

This paper describes Lua, a language for extending applications. Lua combines procedural features with powerful data description facilities, by using a simple, yet powerful, mechanism of tables . This mechanism implements the concepts of records, arrays and recursive data types (pointers), and adds some object-oriented facilities, such as methods with dynamic dispatching. Lua presents a mechanism of fallbacks that allows programmers to extend the semantics of the language in some unconventional ways. As a noteworthy example, fallbacks allow the user to add different kinds of inheritance to the language. Currently, Lua is being extensively used in production for several tasks, including user configuration, general-purpose data-entry, description of user interfaces, storage of structured graphical metafiles, and generic attribute configuration for finite element meshes.