In this paper, we address the following research problem: How can we generate a meaningful split grammar that explains a given facade layout? To evaluate if a grammar is meaningful, we propose a cost function based on the description length and minimize this cost using an approximate dynamic programming framework. Our evaluation indicates that our framework extracts meaningful split grammars that are competitive with those of expert users, while some users and all competing automatic solutions are less successful.
There are several important research questions related to inverse procedural modeling that are complementary to our work. When dealing with noisy input or input that is not segmented, lower level shape understanding, most importantly symmetry detection, is the first important step to inverse procedural modeling @cite_24 @cite_32 . After a set of shape grammars has been learned from typical input facades (e.g., using the method described in this paper), they can be used as priors to guide further reconstruction efforts. This very exciting and important line of work has been picked up by several research groups, e.g., @cite_5 @cite_11 @cite_34 @cite_28 @cite_22 @cite_30 @cite_15 @cite_26 @cite_9 .
@cite_30 @cite_26 @cite_22 @cite_28 @cite_9 @cite_32 @cite_24 @cite_5 @cite_15 @cite_34 @cite_11
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In this paper, we address the problem of inverse procedural modeling: Given a piece of exemplar 3D geometry, we would like to find a set of rules that describe objects that are similar to the exemplar. We consider local similarity, i.e., each local neighborhood of the newly created object must match some local neighborhood of the exemplar. We show that we can find explicit shape modification rules that guarantee strict local similarity by looking at the structure of the partial symmetries of the object. By cutting the object into pieces along curves within symmetric areas, we can build shape operations that maintain local similarity by construction. We systematically collect such editing operations and analyze their dependency to build a shape grammar. We discuss how to extract general rewriting systems, context free hierarchical rules, and grid-based rules. All of this information is derived directly from the model, without user interaction. The extracted rules are then used to implement tools for semi-automatic shape modeling by example, which are demonstrated on a number of different example data sets. Overall, our paper provides a concise theoretical and practical framework for inverse procedural modeling of 3D objects. We present an important step towards the solution of the problem of inverse procedural modeling by generating parametric context-free L-systems that represent an input 2D model. The L-systemrules efficiently code the regular structures and the parameters represent the properties of the structure transformations. The algorithm takes as input a 2D vector image that is composed of atomic elements, such as curves and poly-lines. Similar elements are recognized and assigned terminal symbols ofan L-systemalphabet. Theterminal symbols’ position and orientation are pair-wise compared and the transformations are stored as points in multiple 4D transformation spaces. By careful analysis of the clusters in the transformation spaces, we detect sequences of elements and code them as L-system rules. The coded elements are then removed from the clusters, the clusters are updated, and then the analysis attempts to code groups of elements in (hierarchies) the same way. The analysis ends with a single group of elements that is coded as an L-system axiom. We recognize and code branching sequences of linearly translated, scaled, and rotated elements and their hierarchies. The L-system not only represents the input image, but it can also be used for various editing operations. By changing the L-system parameters, the image can be randomized, symmetrized, and groups of elements and regular structures can be edited. By changing the terminal and non-terminal symbols, elements or groups of elements can be replaced. We propose a novel grammar-driven approach for reconstruction of buildings and landmarks. Our approach complements Structure-from-Motion and image-based analysis with a 'inverse' procedural modeling strategy. So far, procedural modeling has mostly been used for creation of virtual buildings, while the inverse approaches typically focus on reconstruction of single facades. In our work, we reconstruct complete buildings as procedural models using template shape grammars. In the reconstruction process, we let the grammar interpreter automatically decide on which step to take next. The process can be seen as instantiating the template by determining the correct grammar parameters. As an example, we have chosen the reconstruction of Greek Doric temples. This process significantly differs from single facade segmentation due to the immediate need for 3D reconstruction. We propose a novel approach to both learning and detecting local contour-based representations for mid-level features. Our features, called sketch tokens, are learned using supervised mid-level information in the form of hand drawn contours in images. Patches of human generated contours are clustered to form sketch token classes and a random forest classifier is used for efficient detection in novel images. We demonstrate our approach on both top-down and bottom-up tasks. We show state-of-the-art results on the top-down task of contour detection while being over 200x faster than competing methods. We also achieve large improvements in detection accuracy for the bottom-up tasks of pedestrian and object detection as measured on INRIA and PASCAL, respectively. These gains are due to the complementary information provided by sketch tokens to low-level features such as gradient histograms.