Normal meshes are new fundamental surface descriptions inspired by differential geometry. A normal mesh is a multiresolution mesh where each level can be written as a normal offset from a coarser version. Hence the mesh can be stored with a single float per vertex. We present an algorithm

Abstract not found

Digital normal meshes in image compression

Normal meshes were recently introduced as a new way to represent geometry. A normal mesh is a multiresolution representation which has the property that all details lie in a known normal direction and hence the mesh depends only on a single scalar per vertex. Such meshes are ideally suited

... In this paper we propose a novel method to approximate a given mesh with a normal mesh. Instead of building an associated parameterization on the fly we assume a globally smooth parameterization at the beginning and cast the problem as one of perturbing this parameterization. Controlling

this article, we propose a novel method to approximate a given mesh with a normal mesh. Instead of building an associated parameterization on the fly, we assume a globally smooth parameterization at the beginning and cast the problem as one of perturbing this parameterization. Controlling

Abstract not found

Highly detailed geometric models are rapidly becoming commonplace in computer graphics. These models, often represented as complex triangle meshes, challenge rendering performance, transmission bandwidth, and storage capacities. This paper introduces the progressive mesh (PM) representation, a new

We propose a new algorithm for compressing three-dimensional triangular mesh data used for representing surfaces. We apply the Estimation-Quantization (EQ) algorithm originally designed for still image compression to the normal mesh wavelet coefficients. The EQ algorithm models the wavelet

Abstract This paper proposes a nonlinear transform based on normal meshes to sparsely represent digital geometrical images consisting of smoothly colored regions separated from each other by smooth contours. The approximation behavior on these images is superior to wavelet methods