Catálogo de publicaciones - libros

Although lots of works have been engaged in interactive and realistic rendering of translucent materials, efficient processing for deformable models remains a challenging problem. In this paper we introduce an approximate image-space approach for real-time rendering of deformable translucent models by taking account of diffuse multiple sub-surface scattering. We decompose the process into two stages, called the Gathering and Scattering corresponding to the computations for incident and exiting irradiance respectively. We derive a simplified all-frequency illumination model for the gathering of the incident irradiance, which is amenable for deformable models using two auxiliary textures. We introduce two modes for efficient accomplishment of the view-dependent scattering . We implement our approach by fully exploiting the capabilities of graphics processing units (GPUs). Our implementation achieves visually plausible results and real-time frame rates for deformable models on commodity desktop PCs.

In this paper, a new technique is presented for interactive rendering of caustics fully processed on GPU. Without any pre-computation required, the algorithm can directly render refractive caustics from complex deformable transparent objects onto an opaque receiver surface. By the technique we accurately trace the path of the photons and calculate the energy carried by the photons emitted from the light source, and distribute the energy onto the receiver surface according to Gauss basis function. As a result, photorealistic caustic image is calculated without post-processing and temporal filtering over recent frames. We demonstrate that the interactive caustics can be rendered by our method in real-time for non-uniform deformation of both refractive object and receiver surface, and at the same time, interactive change of light and camera in terms of position and direction could be made.

Monte Carlo is the only choice for a physically correct method to compute the problem of global illumination in the field of realistic image synthesis. Adaptive sampling is an interesting means to reduce noise, which is one of the major problems of general Monte Carlo global illumination algorithms. In this paper, we make use of the fuzzy uncertainty existing in image synthesis and exploit the formal concept of fuzziness in fuzzy set theory to evaluate pixel quality to run adaptive sampling efficiently. Experimental results demonstrate that our novel method can perform significantly better than classic ones. To our knowledge, this is the first application of the fuzzy technique to global illumination image synthesis problems.

Manifold parameterization considers the problem of parameterizing a given triangular mesh onto another mesh surface, which could be particularly plane or sphere surfaces. In this paper we propose a unified framework for manifold parameterization between arbitrary meshes with identical genus. Our approach does this task by directly mapping the connectivity of the source mesh onto the target mesh surface without any intermediate domain and partition of the meshes. The connectivity graph of source mesh is used to approximate the geometry of target mesh using least squares meshes. A subset of user specified vertices are constrained to have the geometry information of the target mesh. The geometry of the mesh vertices is reconstructed while approximating the known geometry of the subset by positioning each vertex approximately at the center of its immediate neighbors. This leads to a sparse linear system which can be effectively solved. Our approach is simple and fast with less user interactions. Many experimental results and applications are presented to show the applicability and flexibility of the approach.

In this paper, we apply Nyström method, a sub-sampling and reconstruction technique, to speed up spectral mesh processing. We first relate this method to Kernel Principal Component Analysis (KPCA). This enables us to derive a novel measure in the form of a matrix trace, based soly on sampled data, to quantify the quality of Nyström approximation. The measure is efficient to compute, well-grounded in the context of KPCA, and leads directly to a greedy sampling scheme via trace maximization. On the other hand, analyses show that it also motivates the use of the max-min farthest point sampling, which is a more efficient alternative. We demonstrate the effectiveness of Nyström method with farthest point sampling, compared with random sampling, using two applications: mesh segmentation and mesh correspondence.

In this paper, we present a novel algorithm that extracts feature curves from triangular mesh domains. It is an extension of the level-set formulation of active contour model in image space to triangular mesh domains. We assume that meshes handled by our method are smooth overall, and feature curves of meshes are thin regions rather than mathematical curves such as found in mechanical parts. We use a simple and robust scheme that assigns feature weights to the vertices of a mesh. We define the energy functional of the active contour over the domain of triangular mesh and derive a level-set evolution equation that finds feature regions. The feature regions are skeletonized and smoothed to form a set of smooth feature curves on the mesh.

In this paper, we introduce a novel approach to denoise meshes taking the balanced flow equation as the theoretical foundation.The underlying model consists of an anisotropic diffusion term and a forcing term. The balance between these two terms is made in a selective way allowing prominent surface features and other details of the meshes to be treated in different ways. The forcing term keeps smoothed surface close to the initial surface.Thus the volume is preserved, and most important, the shape distortion is prevented. Applying a dynamic balance technique, the equation converges to the solution quickly meanwhile generating a more faithful approximation to the original noisy mesh. Our smoothing method maintains simplicity in implementation and numerical results show its high performance.

In this paper, we propose a novel U-System-based approach for representing and matching similar shapes. U-system is a complete orthogonal piecewise k -degree polynomials in L ^2[0,1]and it has some good properties,such as regeneration,convergence by group. Using U-system with finite items, it can be realized to accurate representation of shapes. This paper make shapes analysis in theory. We experimentally demonstrate that U descriptors are more suitable for representing and matching 2D shapes than Fourier descriptors.

This paper proposes a novel shape representation “electric force features”, which is based on electric field theory. This representation has several benefits. First, it is invariant to scale and rigid transform. Second, it can represent complex and ill-defined models because of its physical background. 3D model supposed as charged body, we get the electric field force distribution by placing some testing charges around the 3D model. The force distribution is the feature of the 3D model. Orientation invariance is achieved by calculating the spherical harmonic transform of this distribution. The experiments illuminate that this representation has high discriminating power.

This paper presents a novel data hiding algorithm for 3D polygonal models whose vertices have given normal vectors. The key idea of our algorithm is to embed messages by adjusting the normal vector of a vertex according to the pivot vector. All vertices in a 3D model can be embedded with multiple-bit payloads by using multiple pivot vectors. The distortion is measured by the angle between the adjusted normal vector and the original normal vector to distortion. A distortion coefficient is also introduced to control the distortion rate during the embedding process. Experimental results demonstrate that our algorithm can achieve high data capacity (up to 12 bits per normal vector), and is robust against rotation, translation, and uniform scaling attacks.