Street Generation for City Modelling

International audienceIn this paper, we present a complete solution for automatically retrieving the street graph of an urban model. Given a set of 2-5D polygons representing the buildings footprints and their heights, the algorithm constructs a graph that represent the street network (a node for a crossing, an edge a street, each associated with a set of surrounding buildings) along with geometric information such as the width of the streets. We demonstrate how this graph can be used to analyze the city structure and give an example of its use with an automatic geometric modeler for city streets

Erosion Based Visibility Preprocessing

International audienceThis paper presents a novel method for computing visibility in 2.5D environments. It is based on a novel theoretical result: the visibility from a region can be conservatively estimated by computing the visibility from a point using appropriately "shrunk" occluders and occludees. We show how approximated but yet conservative shrunk objects can efficiently be computed in a urban environment. The application of this theorem provides a tighter potentially visible set (PVS) than the original method it is built on. Finally, theoretical implications of the theorem are discussed, and we believe it can open new research directions

Visualization of Industrial Structures with Implicit GPU Primitives

International audienceWe present a method to interactively visualize large industrial models by replacing most triangles with implicit GPU primitives: cylinders, cone and torus slices. After a reverse-engineering process that recovers these primitives from triangle meshes, we encode their implicit parameters in a texture that is sent to the GPU. In rendering time, the implicit primitives are visualized seamlessly with other triangles in the scene. The method was tested on two massive industrial models, achieving better performance and image quality while reducing memory use

Street Generation for City Modelling

International audienceIn this paper, we present a complete solution for automatically retrieving the street graph of an urban model. Given a set of 2-5D polygons representing the buildings footprints and their heights, the algorithm constructs a graph that represent the street network (a node for a crossing, an edge a street, each associated with a set of surrounding buildings) along with geometric information such as the width of the streets. We demonstrate how this graph can be used to analyze the city structure and give an example of its use with an automatic geometric modeler for city streets

Erosion Based Visibility Preprocessing

International audienceThis paper presents a novel method for computing visibility in 2.5D environments. It is based on a novel theoretical result: the visibility from a region can be conservatively estimated by computing the visibility from a point using appropriately "shrunk" occluders and occludees. We show how approximated but yet conservative shrunk objects can efficiently be computed in a urban environment. The application of this theorem provides a tighter potentially visible set (PVS) than the original method it is built on. Finally, theoretical implications of the theorem are discussed, and we believe it can open new research directions

Billboard Clouds

We introduce billboard clouds -- a new approach for extreme simplification. Models are simplified onto a set of planes with texture and transparency maps. We present an optimization approach to build a billboard cloud given a geometric error threshold. After computing an appropriate density function in plane space, a greedy approach is used to select suitable representative planes. A very good surface approximation is ensured by favoring planes that are «nearly tangent» to the model. This method does not require connectivity information, but still avoids cracks by projecting primitives onto multiple planes when needed. The technique is quite flexible through the appropriate choice of error metrics, which can include image-space or object-space deformation, as well as any application-dependent objective function. It is fully automatic and controlled by two intuitive user-supplied parameters. For extreme simplification, our approach combines the strengths of mesh decimation and image-based impostors. We demonstrate our technique on a large class of models, including smooth manifolds and composite objects, as well as entire scenes containing buildings and vegetation

Sprite tree: an efficient image-based representation for networked virtual environments

International audienceIn this paper we present a pipeline for automatic analysis of neuronal morphology: from detection, modeling to digital reconstruction. First, we present an automatic, unsupervised object detection framework using stochastic marked point process. It extracts connected neuronal networks by fitting special configuration of marked objects to the centreline of the neurite branches in the image volume giving us position, local width and orientation information. Semantic modeling of neuronal morphology in terms of critical nodes like bifurcations and terminals, generates various geometric and morphology descriptors such as branching index, branching angles, total neurite length, internodal lengths for statistical inference on characteristic neuronal features. From the detected branches we reconstruct neuronal tree morphology using robust and efficient numerical fast marching methods. We capture a mathematical model abstracting out the relevant position, shape and connectivity information about neuronal branches from the microscopy data into connected minimum spanning trees. Such digital reconstruction is represented in standard SWC format, prevalent for archiving, sharing, and further analysis in the neuroimaging community. Our proposed pipeline outperforms state of the art methods in tracing accuracy and minimizes the subjective variability in reconstruction, inherent to semi-automatic methods