Inverse Procedural Modelling of Trees

Procedural tree models have been popular in computer graphics for their ability to generate a variety of output trees from a set of input parameters and to simulate plant interaction with the environment for a realistic placement of trees in virtual scenes. However, defining such models and their parameters is a difficult task. We propose an inverse modelling approach for stochastic trees that takes polygonal tree models as input and estimates the parameters of a procedural model so that it produces trees similar to the input. Our framework is based on a novel parametric model for tree generation and uses Monte Carlo Markov Chains to find the optimal set of parameters. We demonstrate our approach on a variety of input models obtained from different sources, such as interactive modelling systems, reconstructed scans of real trees and developmental models.     

Variational Tree Synthesis

Modelling trees according to desired shapes is important for many applications. Despite numerous methods having been proposed in tree modelling, it is still a non‐trivial task and challenging. In this paper, we present a new variational computing approach for generating realistic trees in specific shapes. Instead of directly modelling trees from symbolic rules, we formulate the tree modelling as an optimization process, in which a variational cost function is iteratively minimized. This cost function measures the difference between the guidance shape and the target tree crown. In addition, to faithfully capture the branch structure of trees, several botanical factors, including the minimum total branches volume and spatial branches patterns, are considered in the optimization to guide the tree modelling process. We demonstrate that our approach is applicable to generate trees with different shapes, from interactive design and complex polygonal meshes.     

Robust Treatment of Degenerate Elements in Interactive Corotational FEM Simulations

We address the problem of robust and efficient treatment of element collapse and inversion in corotational FEM simulations of deformable objects in two and three dimensions, and show that existing degeneration treatment methods have previously unreported flaws that seriously threaten robustness and physical plausibility in interactive applications. We propose a new method that avoids such flaws, yields faster and smoother degeneration recovery and extends the range of well‐behaved degenerate configurations without adding significant complexity or computational cost to standard explicit and quasi‐implicit solvers.     

4D MRI Flow Coupled to Physics‐Based Fluid Simulation for Blood‐Flow Visualization

Modern MRI measurements deliver volumetric and time‐varying blood‐flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data considerably. Nevertheless, the data remains compromised by noise and a lack of spatiotemporal resolution. Besides imaging data, also numerical simulations are employed. These are based on mathematical models of specific features of physical reality. However, these models require realistic parameters and boundary conditions based on measurements. We propose to use data assimilation to bring measured data and physically‐based simulation together, and to harness the mutual benefits. The accuracy and noise robustness of the coupled approach is validated using an analytic flow field. Furthermore, we present a comparative visualization that conveys the differences between using conventional interpolation and our coupled approach.     

Woodification: User‐Controlled Cambial Growth Modeling

We present a botanical simulation of secondary (cambial) tree growth coupled to a physical cracking simulation of its bark. Whereas level set growth would use a fixed resolution voxel grid, our system extends the deformable simplicial complex (DSC), supporting new biological growth functions robustly on any surface polygonal mesh with adaptive subdivision, collision detection and topological control. We extend the DSC with temporally coherent texturing, and surface cracking with a user‐controllable biological model coupled to the stresses introduced by the cambial growth model.     

Understanding risk-taking behavior of groups: A “decision analysis” perspective

Past research has extensively investigated the effect of media, especially focusing on how anonymity increases risk-related behaviors of groups when using computer-mediated communication (CMC). This study extends prior research by examining the differences in group risk-taking behaviors between face-to-face groups and completely non-anonymous CMC groups (i.e., groups working in a fully identified, synchronous CMC environment similar to popular instant messaging systems utilized widely within organizations). Drawing on the “decision analysis” perspective, a key framework for understanding organizational decision-making, the study also examines the effects of the firm's risk preferences as well as the type of information distribution among group members (i.e., full information known to all group members versus partial information know by only some of the members) on the groups' risk-taking behaviors. Results from a laboratory experiment using student subjects found no differences in risk-taking behaviors between CMC and face-to-face groups; additionally, no differences were found related to how information was distributed among group members. A significant effect was found, however, for the risk preference of the firm, showing that risk-neutral firms influenced groups to make riskier decisions than groups from risk-averse firms. Finally, groups within risk-neutral firms receiving partial information made riskier decisions than groups receiving full information. The implications of these results for future research and practice are examined.     

Probabilistic Marching Cubes

In this paper we revisit the computation and visualization of equivalents to isocontours in uncertain scalar fields. We model uncertainty by discrete random fields and, in contrast to previous methods, also take arbitrary spatial correlations into account. Starting with joint distributions of the random variables associated to the sample locations, we compute level crossing probabilities for cells of the sample grid. This corresponds to computing the probabilities that the well‐known symmetry‐reduced marching cubes cases occur in random field realizations. For Gaussian random fields, only marginal density functions that correspond to the vertices of the considered cell need to be integrated. We compute the integrals for each cell in the sample grid using a Monte Carlo method. The probabilistic ansatz does not suffer from degenerate cases that usually require case distinctions and solutions of ill‐conditioned problems. Applications in 2D and 3D, both to synthetic and real data from ensemble simulations in climate research, illustrate the influence of spatial correlations on the spatial distribution of uncertain isocontours.     

青东凹陷油藏类型及主控因素浅析

青东凹陷具有独特而复杂的成藏特征,通过基础地质的综合研究,识别出青东凹陷发育构造、地层、岩性和复合油藏等四类油藏类型,阐述了各种不同油藏类型的分布特征;通过典型油气藏解剖,明确了有效烃源岩、储盖组合、油气输导条件等油气成藏要素和主控因素,研究认为青东凹陷具有双源供烃的油源条件,发育6套储盖组合,骨架砂体和输导断层为油气运移的有效通道;将青东凹陷划分为西北部斜坡、西南部断阶、中部深洼和东部走滑断裂四个油气聚集带,并解剖了各个油气聚集带的成藏类型及成藏主控因素。     

含油气盆地中深层碎屑岩储层异常高孔带研究进展

异常高孔带是指由于经历异常地质作用而形成的孔隙度高于正常压实条件下最大孔隙度的异常高孔隙度储层相对集中发育带,是含油气盆地中深层油气勘探的甜点。国内外学者对其开展了大量有益的研究,总结其研究成果对于指导深部油气藏勘探有着重要的意义。首先总结了近些年全球范围内异常高孔带的研究现状,主要包括异常高孔带概念、划分方案、类型、储集空间、成因机制及预测等。研究表明,异常高孔带的分布通常利用孔隙度正常压实趋势线(或相当的趋势线)进行确定。根据孔隙类型及含量,异常高孔带具有原生孔隙型异常高孔带和次生孔隙型异常高孔带2种类型,原生孔隙型异常高孔带储集空间以原生孔隙为主,保孔型地质因素是其形成的主控因素;次生孔隙型异常高孔带储集空间以次生孔隙为主,溶蚀增孔作用是其形成的主控因素。然后对中国含油气盆地深层异常高孔带的特征和成因机制进行了系统总结,认为其分布广泛,类型多样;其分布具有地层深度范围大、时代跨越尺度大、盆地类型多样和沉积相类型丰富等特点;其类型兼具原生孔隙型和次生孔隙型。最后指出了目前研究中存在的一些问题和今后的研究趋势。     

论家具动态设计之折叠结构类型

本文论述了家具动态设计与折叠的概念与意义,并以动态设计中最常见的动态结构类型——折叠为例,探讨了动态家具设计的原理与类型.