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Arash HABIB1 ACROE Association pour la Creation et la Recherche sur les Outils dExpression INPG -46 av. F6lix Viallet, ABSTRACTThis paper deals with the modeling of loose soil (sandy, muddy, etc.) When an object moves on such grounds, the object's and the soil's movement both depend the mutual physical interactions, and therefore are very dificult to achieve with kinematic or geometric models.We use a particle-based dynamic modeler and achieve a discrete model of plasticity, which accounts for the influence of the soil on objects moving on this soil, but also for the influence of the object on the movement and the shape of the soil. Thus we have simulated soil compression and piling, vehicles leaving tire traces, spinning, skidding and even sinking. This first step is the simulation of the soil-object system at a discretization scale that can be termed "intermediate". A subsequent step consists of the simulation of a finer physical soil model in order to account for smaller-scale dynamic phenomena.

Figure 1: Reconstruction of a flower model from a sketch: the input sketch (a), the guide strokes provided by the user which will be used for the 3D cone reconstruction (b), the segmented sketch into petals and other botanical elements (c), the reconstructed model from the same point of view as the input (d); and from a different view (e).

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In this paper, we present a sketch-based modeler that reconstructs a 3D shape by combining a single descriptive sketch and minimal user intervention. The user provides a single 2D drawing in the form of a descriptive sketch, where solid curves describe the visible silhouette, and dashed curves the hidden outline. The curves are partitioned into a set of closed curves in a semi-automatic manner, each of which is consolidated into a closed surface element by solving a constrained optimization problem. The final 3D shape is generated by assembling these surface elements. The algorithmic reconstruction is complemented by allowing users to optionally guide the shape computation or correct any inaccuracy. This is done by successively specifying different kinds of local constraints on sparsely selected points in rotated views, such as adjustment of volume thickness along the projection line, or curvature discontinuity. Consequently, the range and complexity of shapes that can be created from a single-view sketch are significantly extended. We evaluate our solution by reconstructing a wide range of 3D models from sketches of various sources, and visually comparing the reference models and the shapes reconstructed by users.

To produce impressive virtual worlds, real-time crowd simulations require large and detailed scenes populated by agents with complex shapes and geometry. For efficiency reasons, these agents are usually approximated by point-like representations to optimize the performances of collision avoidance and interactions between agents. This paper addresses the issue of handling deformable polygonal agents with arbitrary shapes in real time crowd simulations. The proposed multiresolution framework supports environments with arbitrary topologies and provides tools for efficient proximity queries.

The use of cost-efficient and portative devices, as the Kinect, enables the easy reconstruction of interior scenes. Thus 3D reconstruction has become a field of interest to many researchers. A large number of applications have thus been created, seeking to provide the most accurate reconstruction of a scene through a complete 3D scan. Although these applications are efficient, the scanning of the scene by the user remains a time-consuming process. Moreover this process tends to be difficult for users since the scan required by those methods must not contain any missing data. Our work, on the other hand, requires only a few shots without any overlapping requirement, which makes the scanning process very fast. Furthermore our method works also on shots obtained from virtual scenes which makes it able to create new architectures by assembling shots from different real or virtual scenes. Since we are working with sparse data, our reconstruction will be less detailed than a reconstruction based on a full scan.