João Paulo Gois

Hermite Radial Basis Functions Implicits

The Hermite radial basis functions (HRBF) implicits reconstruct an implicit function which interpolates or approximates scattered multivariate Hermite data (i.e. unstructured points and their corresponding normals). Experiments suggest that HRBF implicits allow the reconstruction of surfaces rich in details and behave better than previous related methods under coarse and/or non‐uniform samplings, even in the presence of close sheets. HRBF implicits theory unifies a recently introduced class of surface reconstruction methods based on radial basis functions (RBF), which incorporate normals directly in their problem formulation. Such class has the advantage of not depending on manufactured offset‐points to ensure existence of a non‐trivial implicit surface RBF interpolant. In fact, we show that HRBF implicits constitute a particular case of Hermite–Birkhoff interpolation with radial basis functions, whose main results we present here. This framework not only allows us to show connections between the present method and others but also enable us to enhance the flexibility of our method by ensuring well‐posedness of an interesting combined interpolation/regularization approach.

Hermite Interpolation of Implicit Surfaces with Radial Basis Functions

We present the Hermite radial basis function (HRBF) implicits method to compute a global implicit function which interpolates scattered multivariate Hermite data (unstructured points and their corresponding normals). Differently from previous radial basis functions (RBF) approaches, HRBF implicits do not depend on offset points to ensure existence and uniqueness of its interpolant. Intrinsic properties of this method allow the computation of implicit surfaces rich in details, with irregularly spaced points even in the presence of close sheets. Comparisons to previous works show the effectiveness of our approach. Further, the theoretical background of HRBF implicits relies on results from generalized interpolation theory with RBFs, making possible powerful new variants of this method and establishing connections with previous efforts based on statistical learning theory.

Generalized Hermitian Radial Basis Functions Implicits from polygonal mesh constraints

In this work we investigate a generalized interpolation approach using radial basis functions to reconstruct implicit surfaces from polygonal meshes. With this method, the user can define with great flexibility three sets of constraint interpolants: points, normals, and tangents; allowing to balance computational complexity, precision, and feature modeling. Furthermore, this flexibility makes possible to avoid untrustworthy information, such as normals estimated on triangles with bad aspect ratio. We present results of the method for applications related to the problem of modeling 2D curves from polygons and 3D surfaces from polygonal meshes. We also apply the method to problems involving subdivision surfaces and front-tracking of moving boundaries. Finally, as our technique generalizes the recently proposed HRBF Implicits technique, comparisons with this approach are also conducted.

Robust and Adaptive Surface Reconstruction using Partition of Unity Implicits

Implicit surface reconstruction from unorganized point sets has been recently approached with methods based on multi-level partition of unity. We improve this approach by addressing local approximation robustness and iso-surface extraction issues. Our method relies on the J1 A triangulation to perform both the spatial subdivision and the iso-surface extraction. We also make use of orthogonal polynomials to provide adaptive local approximations in which the degree of the polynomial can be adjusted to accurately reconstruct the surface locally. Finally, we compare our results with previous work to demonstrate the robustness of our method.

Resampling Strategies for Deforming MLS Surfaces

Moving‐least‐squares (MLS) surfaces undergoing large deformations need periodic regeneration of the point set (point‐set resampling) so as to keep the point‐set density quasi‐uniform. Previous work by the authors dealt with algebraic MLS surfaces, and proposed a resampling strategy based on defining the new points at the intersections of the MLS surface with a suitable set of rays. That strategy has very low memory requirements and is easy to parallelize. In this article new resampling strategies with reduced CPU‐time cost are explored. The basic idea is to choose as set of rays the lines of a regular, Cartesian grid, and to fully exploit this grid: as data structure for search queries, as spatial structure for traversing the surface in a continuation‐like algorithm, and also as approximation grid for an interpolated version of the MLS surface. It is shown that in this way a very simple and compact resampling technique is obtained, which cuts the resampling cost by half with affordable memory requirements.

Interactive Graphics Applications with OpenGL Shading Language and Qt

Qt framework allows the easy development of professional cross-platform graphics applications using C++. Qt provides the QtOpenGL Module that makes easy the development of hardware-accelerated graphics applications using OpenGL and OpenGL Shading Language (GLSL). With Qt, matrices, vectors, vertex buffer objects, textures, shader programs and UI components are integrated by classes in the object-oriented paradigm and intercommunicate by the Qt mechanism of signals/slots. The goal of this survey is to detail the development of interactive graphics applications with OpenGL and Qt. Along with it, we compare features of QtOpenGL Module with those of GLU/GLUT libraries, as the latter is traditionally used in text books and computer graphics courses.

A low-cost-memory CUDA implementation of the conjugate gradient method applied to globally supported radial basis functions implicits

Abstract Hermitian radial basis functions implicits is a method capable of reconstructing implicit surfaces from first-order Hermitian data. When globally supported radial functions are used, a dense symmetric linear system must be solved. In this work, we aim at exploring and computing a matrix-free implementation of the Conjugate Gradients Method on the GPU in order to solve such linear system. The proposed method parallelly rebuilds the matrix on demand for each iteration. As a result, it is able to compute the Hermitian-based interpolant for datasets that otherwise could not be handled due to the high memory demanded by their linear systems.

Development of a Gesture-Based Game Applying Participatory Design to Reflect Values of Manual Wheelchair Users

Wheelchair users have been benefited from Natural User Interface (NUI) games because gesture-based applications can help motor disabled people. Previous work showed that considering values and the social context of these users improve game enjoyment. However, the literature lacks on studies that address games as a tool to approach personal values of people with physical disabilities. Participatory design encompasses techniques that allow absorbing and reflecting values of users into technologies. We developed a gesture-based game using participatory design addressing values of wheelchair users. To manage the development of our game, we permitted creativity and flexibility to the designers. Our design is aligned to the Game SCRUM and make use of concepts from the Creative Process. The products of each stage of the design that we applied are both a gesture-based game and its evaluation. We tested the enjoyment (immersion, difficult while playing, etc.) of users for the game that we developed thought game-based quantitative and qualitative analyses. Our results indicate that the game was able to provide a satisfactory entertaining experience to the users.

Contour-Aware 3D Reconstruction of Side-View Sketches

Abstract The 3D reconstruction from a single 2D side-view sketch faces challenges in capturing details of the curves and inferring the hidden parts. Here, we introduce an approach that ensures the 2D contours are interpolated to a suitable 3D reconstruction while capturing the small details of the sketch. To this end, we propose a novel strategy that combines a proper 3D Hermitian data generation from the vicinity of the sketches with an approach for identification and completion of sketch curves. Feasible 3D models are then generated using Hermitian Radial Basis Functions (HRBF) Implicit Surfaces. Results indicate that our approach provides not only more detailed 3D reconstructed models but also more perceptual agreement from the input sketches in comparison to previous work.

Puppeteering 2.5D Models

A laborious task for animators is the redrawing of 2D models for each new required view or pose. As a consequence, several applications have been proposed to make this task easier. A successful approach is the Cartoon 2.5D Models. Its goal is the automatic computation of new views - by the simulation of 3D global rotation - from user-provided 2D models. However, previous work of 2.5D Models does not have support to calculate new poses efficiently, i.e., the user redraws the input views again in the new pose. We present a novel approach that allows the user to produce both new views and new poses easily for the 2.5D Models, thus puppeteering the 2.5D Models. It makes use of a hierarchical bone structure that explores the methodology of the 2.5D Models, ensuring coherence between the bone structure and the model. The usability of the present approach is intuitive for users acquainted with previous 2.5D Modeling tools.