By Georgios Miaoulis (auth.), Georgios Miaoulis, Dimitri Plemenos (eds.)
This ebook is devoted to clever scene modeling details structures, that's info structures utilizing man made Intelligence strategies to layout scenes. Declarative scene modeling recommendations are awarded, in addition to their implementation in an clever info system.
In order to enhance potency of declarative modeling established scene modeling details structures, a number of innovations are proposed within the booklet: coupling of a declarative modeler with a classical geometric modeler. Use of machine-learning man made clever ideas, permitting the process to benefit the consumer personal tastes; creation of excessive point suggestions, particularly the concept that of "style" in architectural layout; creation of collaborative declarative modeling techniques.
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Each box is placed in the 3D space and, if the user does not like the proposed box and placement, another solution Fig. 9. Boxes arrangement and form growth with VoluFormes 38 D. Plemenos can be given. Once the current box is placed in the space, the same process is applied to define the next one. The generation engine of Voluboites uses a CSP-like constraint satisfaction technique. On the left of Fig. 9, one can see a boxes arrangement obtained by Voluboites. Voluscenes is used by the designer to put germs in the boxes and to select the growth mechanism, among a reduced number of predefined imperative mechanisms.
Thus, PolyFormes [6,7] is a declarative modeler designed to generate regular or semi-regular polyhedrons. The main advantage of the dedicated declarative modelers is efficiency because their solution generation engine can be well adapted to the properties of the specific modeling area covered by the modeler. On the other hand, it is difficult for such a modeler to evolve in order to be able to process another specific modeling area. The aim of the general purpose modelers is generality. These modelers include a solution generation engine which can process several kinds of properties, together with a reduced set of pre-defined properties, as general as possible.
17, backtracking is performed to node C, without exploring all the remaining possibilities for nodes D and E. Intelligent backtracking is based on independence of various parts of the CSP and these parts are easy to determine with the hierarchical decomposition tree. This independence has been exploited to parallelize the resolution process in MultiFormes. Fig. 18 shows an example of a scene generated with the arithmetic CSP solver of MultiFormes. 2 Geometric Constraint Satisfaction Techniques The main drawback of the use of mainly linear arithmetic constraints to describe properties in MultiFormes is that decomposition is always performed by using bounding boxes of the various parts of the scene, the faces of these boxes being parallel to the plans defined by the main axes.