KAUST Mark Pauly EPFLFigure 1: (Left) Random models generated from a probabilistic building grammar. Although these models are visually plausible, they do not comply with a design scenario which also requires architectural plausibility, i.e. matching styles of ground floors, upper floors, and roofs (B1, see Table 2). (Right) Our framework takes user specified preference scores as input and learns a new model probability density function (pdf) which samples models (with consistent style) proportionally to their predicted preference scores. In this design scenario, office buildings received a higher preference score. AbstractA shape grammar defines a procedural shape space containing a variety of models of the same class, e.g. buildings, trees, furniture, airplanes, bikes, etc. We present a framework that enables a user to interactively design a probability density function (pdf) over such a shape space and to sample models according to the designed pdf. First, we propose a user interface that enables a user to quickly provide preference scores for selected shapes and suggest sampling strategies to decide which models to present to the user to evaluate. Second, we propose a novel kernel function to encode the similarity between two procedural models. Third, we propose a framework to interpolate user preference scores by combining multiple techniques: function factorization, Gaussian process regression, autorelevance detection, and l 1 regularization. Fourth, we modify the original grammars to generate models with a pdf proportional to the user preference scores. Finally, we provide evaluations of our user interface and framework parameters and a comparison to other exploratory modeling techniques using modeling tasks in five example shape spaces: furniture, low-rise buildings, skyscrapers, airplanes, and vegetation. *

Figure 1: Tree L-Systems A design transformation computed from two different L-systems (first and last images). Our method can compute a sequence of intermediate results transforming one design into the other by combining and merging the L-systems rather than the geometry of the two trees. AbstractWe introduce design transformations for rule-based procedural models, e.g., for buildings and plants. Given two or more procedural designs, each specified by a grammar, a design transformation combines elements of the existing designs to generate new designs. We introduce two technical components to enable design transformations. First, we extend the concept of discrete rule switching to rule merging, leading to a very large shape space for combining procedural models. Second, we propose an algorithm to jointly derive two or more grammars, called grammar co-derivation. We demonstrate two applications of our work: we show that our framework leads to a larger variety of models than previous work, and we show fine-grained transformation sequences between two procedural models.

Figure 1: In procedural modeling, a single rule set can produce a wide variety of 3D models (left). This paper presents a thumbnail gallery generation system which automatically samples a rule set, clusters the resulting models into distinct groups (middle), and selects a representative image for each group to visualize the diversity of the rule set (right). AbstractProcedural modeling allows for the generation of innumerable variations of models from a parameterized, conditional or stochastic rule set. Due to the abstractness, complexity and stochastic nature of rule sets, it is often very difficult to have an understanding of the diversity of models that a given rule set defines. We address this problem by presenting a novel system to automatically generate, cluster, rank, and select a series of representative thumbnail images out of a rule set. We introduce a set of 'view attributes' that can be used to measure the suitability of an image to represent a model, and allow for comparison of different models derived from the same rule set. To find the best thumbnails, we exploit these view attributes on images of models obtained by stochastically sampling the parameter space of the rule set. The resulting thumbnail gallery gives a representative visual impression of the procedural modeling potential of the rule set. Performance is discussed by means of a number of distinct examples and compared to state-of-the-art approaches.

No abstract

We describe a technique that uses tractography to visualize neural pathways in human brains by extending an existing framework that uses overlapping Gaussian tensors to model the signal. At each point on the fiber, an unscented Kalman filter is used to find the most consistent direction as a mixture of previous estimates and of the local model. In our previous framework, the diffusion ellipsoid had a cylindrical shape, i.e., the diffusion tensor’s second and third eigenvalues were identical. In this paper, we extend the tensor representation so that the diffusion tensor is represented by an arbitrary ellipsoid. Experiments on synthetic data show a reduction in the angular error at fiber crossings and branchings. Tests on in vivo data demonstrate the ability to trace fibers in areas containing crossings or branchings, and the tests also confirm the superiority of using a full tensor representation over the simplified model.