Session 1: Geometry Processing Preserving Sharp Edges in Geometry Images Mathieu Gauthier, Pierre Poulin A geometry image offers a simple and compact way of encoding the geometry of a surface in an image-liking algorithms generate geometry images by parameterizing the surface onto a domaine data structure. It has been shown to be useful in multiple applications. Most exist, and by performing a regular resampling. Unfortunately, this regular resampling fails to capture sharp features present on the surface. In this paper, we propose to slightly alter the grid to align sample positions with sharp edges and corners in the geometric model. While doing so, our goal is to maintain the resulting geometry images simple to interpret, while producing higher quality reconstructions. We demonstrate an implementation in the planar domain and show results on a range of common geometrical models. Fast Visualization of Complex 3D Models Using Displacement Mapping The-Kiet Lu, Kok-Lim Low, Jianmin Zheng We present a simple method to render complex 3D models at interactive rates using real-time displacement mapping. We use an octree to decompose the 3D model into a set of height fields and display the model by rendering the height fields using per-pixel displacement mapping. By simply using the faces of the octree voxels as base polygons for displacement mapping, and with straightforward transformation of view rays to the displacement map’s local space, our method is able to accurately render the object’s silhouettes with very little special handling. The algorithm is especially suitable for fast visualization of high-detail point-based models, and models made up of unprocessed triangle meshes that come straight from range scanning. This is because our method requires much less preprocessing time compared to the traditional triangle-based rendering approach, which usually needs a large amount of computation to preprocess the input model into one that can be rendered more efficiently. Unlike the point-based rendering approach, the rendering efficiency of our method is not limited by the number of input points. Our method can achieve interactive rendering of models with more than 300 millions points on standard graphics hardware. Session 2: Surfaces and Meshes Fast low-memory streaming MLS reconstruction of point-sampled surfaces Gianmauro Cuccuru, Enrico Gobbetti, Fabio Marton, Renato Pajarola, Ruggero Pintus We present a simple and efficient streaming method for reconstructing triangulated surfaces from massive oriented point sample datasets. The method combines streaming and parallelization, moving least-squares (MLS) projection, adaptive space subdivision, and regularized isosurface extraction. Besides presenting the overall design and evaluation of the system, our contributions include methods for keeping in-