Local spot noise for procedural surface details synthesis

Abstract

To deal with the increasing demand for complex visual details in virtual worlds, procedural methods for content authoring are an expanding field in Computer Graphics. Focusing on on-the-fly texture generation, we present in this paper a content authoring process based on Locally Controlled Spot Noise. Through the control of both the impulses distribution and the spatially-defined kernel, this process can cover a wide range of appearances. In this context, we introduce a new kernel formulation that provides an efficient anisotropic filtering of the generated texture. Furthermore, our method allows users to interactively create the desired appearance by controlling both albedo and meso-geometry of the underlying surface, tackling on-the-fly normal map generation. Our method can be used as an artist friendly tool to model high-quality surface details with direct control over the final appearance in real-time.

Cyclostationary Gaussian noise: theory and synthesis

“…This model is close to a specific kind of locally controlled spot noise presented in Pavie et al [PGDG16] and Cavalier et al [CGG19], where the results can be made regular by controlling the position where the spots are shot: we conjecture that our CS-ADSN is the underlying cyclostationary model for the perfectly regular version of the controlled spot noise they present without consideration for the cyclostationary mean and the variance of the output, which enables us to go one step further and to achieve a byexample synthesis for cyclostationary textures in the next section. 5: Exemplars (top row) synthesized with our cyclostationary spot noise (middle) and our cyclostationary high-performance noise (bottom).…”

“…They are thus unable to synthesize periodic statistics, and subsequently unable to model color transformations in near‐regular textures. Works attempting to control the spatial organization of the underling stationary process do so by either incidentally altering the stationarity [GSDC17, PGDG16, CGG19], by varying the sparse convolution using specific point processes; or, by altering their Gaussian nature through the control of the phase [GSV + 14, TEZ + 19]; or by reducing the convergence towards a Gaussian process [HN18]. Conversely, we propose to alterate stationarity and keep the Gaussian nature by exploiting a generalization to Gaussian cyclostationary processes.…”

“…These works were improved with texton noise [GLM17], which computes a summarized version of the kernel, optimized for the synthesis. The work of Pavie et al [PGDG16] and Cavalier et al [CGG19] show that controlling the position of these spots helps controlling the visual aspect of the output. High-performance noise [HN18, DH18, Bur19] is a very fast method approximating the ADSN model.…”

Algorithm for appearance simulation of plant diseases based on symptom classification

“… Munoz-Pandiella et al (2018) proposed a technique based on a fast physics-inspired method that Ishitobi et al (2020) used a triangular grid to simulate the weathering of a rust-proof coated metal surface after mechanical deterioration in three steps based on fundamental mechanics: “separation-splitting-exfoliation.” In texture representation and synthesis, Guingo et al (2017) propose a two-layer representation of textures, with a noise layer capturing fine Gaussian patterns and a structure layer capturing non-Gaussian patterns and structures, synchronizing the two layers by a set of masks to make them consistent. Cavalier et al (2019) propose a method based on local control of speckle noise by controlling the pulse distribution and a spatially defined kernel to create the desired texture appearance in a user-interactive manner. Due to the essential difference between the object of application and the principle of texture generation, a generation algorithm suitable for plant disease apparent texture needs to be explored on the basis of the reference.…”