Basile Sauvage

On-the-fly multi-scale infinite texturing from example

In computer graphics, rendering visually detailed scenes is often achieved through texturing. We propose a method for on-the-fly non-periodic infinite texturing of surfaces based on a single image. Pattern repetition is avoided by defining patches within each texture whose content can be changed at runtime. In addition, we consistently manage multi-scale using one input image per represented scale. Undersampling artifacts are avoided by accounting for fine-scale features while colors are transferred between scales. Eventually, we allow for relief-enhanced rendering and provide a tool for intuitive creation of height maps. This is done using an ad-hoc local descriptor that measures feature self-similarity in order to propagate height values provided by the user for a few selected texels only. Thanks to the patch-based system, manipulated data are compact and our texturing approach is easy to implement on GPU. The multi-scale extension is capable of rendering finely detailed textures in real-time.

Multi-scale label-map extraction for texture synthesis

Texture synthesis is a well-established area, with many important applications in computer graphics and vision. However, despite their success, synthesis techniques are not used widely in practice because the creation of good exemplars remains challenging and extremely tedious. In this paper, we introduce an unsupervised method for analyzing texture content across multiple scales that automatically extracts good exemplars from natural images. Unlike existing methods, which require extensive manual tuning, our method is fully automatic. This allows the user to focus on using texture palettes derived from their own images, rather than on manual interactions dictated by the needs of an underlying algorithm. Most natural textures exhibit patterns at multiple scales that may vary according to the location (non-stationarity). To handle such textures many synthesis algorithms rely on an analysis of the input and a guidance of the synthesis. Our new analysis is based on a labeling of texture patterns that is both (i) multi-scale and (ii) unsupervised -- that is, patterns are labeled at multiple scales, and the scales and the number of labeled clusters are selected automatically. Our method works in two stages. The first builds a hierarchical extension of superpixels and the second labels the superpixels based on random walk in a graph of similarity between superpixels and a nonnegative matrix factorization. Our label-maps provide descriptors for pixels and regions that benefit state-of-the-art texture synthesis algorithms. We show several applications including guidance of non-stationary synthesis, content selection and texture painting. Our method is designed to treat large inputs and can scale to many megapixels. In addition to traditional exemplar inputs, our method can also handle natural images containing different textured regions.

Local random-phase noise for procedural texturing

Local random-phase noise is a noise model for procedural texturing. It is defined on a regular spatial grid by local noises, which are sums of cosines with random phase. Our model is versatile thanks to separate sampling in the spatial and spectral domains. Therefore, it encompasses Gabor noise and noise by Fourier series. A stratified spectral sampling allows for a faithful yet compact and efficient reproduction of an arbitrary power spectrum. Noise by example is therefore obtained faster than state-of-the-art techniques. As a second contribution we address texture by example and generate not only Gaussian patterns but also structured features present in the input. This is achieved by fixing the phase on some part of the spectrum. Generated textures are continuous and non-repetitive. Results show unprecedented framerates and a flexible visual result: users can control with one parameter the blending between noise by example and structured texture synthesis.

Semi-Regular Triangle Remeshing: A Comprehensive Study

Semi‐regular triangle remeshing algorithms convert irregular surface meshes into semi‐regular ones. Especially in the field of computer graphics, semi‐regularity is an interesting property because it makes meshes highly suitable for multi‐resolution analysis. In this paper, we survey the numerous remeshing algorithms that have been developed over the past two decades. We propose different classifications to give new and comprehensible insights into both existing methods and issues. We describe how considerable obstacles have already been overcome, and discuss promising perspectives.

Robust Fitting on Poorly Sampled Data for Surface Light Field Rendering and Image Relighting

Two‐dimensional (2D) parametric colour functions are widely used in Image‐Based Rendering and Image Relighting. They make it possible to express the colour of a point depending on a continuous directional parameter: the viewing or the incident light direction. Producing such functions from acquired data is promising but difficult. Indeed, an intensive acquisition process resulting in dense and uniform sampling is not always possible. Conversely, a simpler acquisition process results in sparse, scattered and noisy data on which parametric functions can hardly be fitted without introducing artefacts. Within this context, we present two contributions. The first one is a robust least‐squares‐based method for fitting 2D parametric colour functions on sparse and scattered data. Our method works for any amount and distribution of acquired data, as well as for any function expressed as a linear combination of basis functions. We tested our fitting for both image‐based rendering (surface light fields) and image relighting using polynomials and spherical harmonics. The second one is a statistical analysis to measure the robustness of any fitting method. This measure assesses a trade‐off between precision of the fitting and stability with respect to input sampling conditions. This analysis along with visual results confirm that our fitting method is robust and reduces reconstruction artefacts for poorly sampled data while preserving the precision for a dense and uniform sampling.

Simplification of meshes with digitized radiance

View-dependent surface color of virtual objects can be represented by outgoing radiance of the surface. In this paper we tackle the processing of outgoing radiance stored as a vertex attribute of triangle meshes. Data resulting from an acquisition process can be very large and computationally intensive to render. We show that when reducing the global memory footprint of such acquired objects, smartly reducing the spatial resolution is an effective strategy for overall appearance preservation. Whereas state-of-the-art simplification processes only consider scalar or vectorial attributes, we conversely consider radiance functions defined on the surface for which we derive a metric. For this purpose, several tools are introduced like coherent radiance function interpolation, gradient computation, and distance measurements. Both synthetic and acquired examples illustrate the benefit and the relevance of this radiance-aware simplification process.

Visual Quality Assessment of 3D Models: On the Influence of Light-Material Interaction

Geometric modifications of three-dimensional (3D) digital models are commonplace for the purpose of efficient rendering or compact storage. Modifications imply visual distortions that are hard to measure numerically. They depend not only on the model itself but also on how the model is visualized. We hypothesize that the model’s light environment and the way it reflects incoming light strongly influences perceived quality. Hence, we conduct a perceptual study demonstrating that the same modifications can be masked, or conversely highlighted, by different light-matter interactions. Additionally, we propose a new metric that predicts the perceived distortion of 3D modifications for a known interaction. It operates in the space of 3D meshes with the object’s appearance, that is, the light emitted by its surface in any direction given a known incoming light. Despite its simplicity, this metric outperforms 3D mesh metrics and competes with sophisticated perceptual image-based metrics in terms of correlation to subjective measurements. Unlike image-based methods, it has the advantage of being computable prior to the costly rendering steps of image projection and rasterization of the scene for given camera parameters.

Bi-Layer textures: a Model for Synthesis and Deformation of Composite Textures

We propose a bi‐layer representation for textures which is suitable for on‐the‐fly synthesis of unbounded textures from an input exemplar. The goal is to improve the variety of outputs while preserving plausible small‐scale details. The insight is that many natural textures can be decomposed into a series of fine scale Gaussian patterns which have to be faithfully reproduced, and some non‐homogeneous, larger scale structure which can be deformed to add variety. Our key contribution is a novel, bi‐layer representation for such textures. It includes a model for spatially‐varying Gaussian noise, together with a mechanism enabling synchronization with a structure layer. We propose an automatic method to instantiate our bi‐layer model from an input exemplar. At the synthesis stage, the two layers are generated independently, synchronized and added, preserving the consistency of details even when the structure layer has been deformed to increase variety. We show on a variety of complex, real textures, that our method reduces repetition artifacts while preserving a coherent appearance.