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: Structure-aware facade editing. Using the notion of generalized grids, our system encodes various symmetry, alignment, and hierarchy relations among the elements of a facade. During incremental editing, the user can specify different grids (shown as box abstractions) for which our system proposes new configurations. Editing progresses by selecting such grids and one of the proposed configurations (shown in red). AbstractMany man-made objects, in particular building facades, exhibit dominant structural relations such as symmetry and regularity. When editing these shapes, a common objective is to preserve these relations. However, often there are numerous plausible editing results that all preserve the desired structural relations of the input, creating ambiguity. We propose an interactive facade editing framework that explores this structural ambiguity. We first analyze the input in a semi-automatic manner to detect different groupings of the facade elements and the relations among them. We then provide an incremental editing process where a set of variations that preserve the detected relations in a particular grouping are generated at each step. Starting from one input example, our system can quickly generate various facade configurations.

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Understanding patterns of variation from raw measurement data remains a central goal of shape analysis. Such an understanding reveals which elements are repeated, or how elements can be derived as structured variations from a common base element. We investigate this problem in the context of 3D acquisitions of buildings. Utilizing a set of template models, we discover geometric similarities across a set of building elements. Each template is equipped with a deformation model that defines variations of a base geometry. Central to our algorithm is a simultaneous template matching and deformation analysis that detects patterns across building elements by extracting similarities in the deformation modes of their matching templates. We demonstrate that such an analysis can successfully detect structured variations even for noisy and incomplete data.

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