Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Gabriele Gorla

dblp:56/312 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2003
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visual content generation and editing · 46% Visualization and visual analytics · 23% Computational photography and imaging · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visual content generation and editing › texture synthesis
3d texture synthesis
0.012003
Texture Synthesis for 3D Shape Representation · IEEE Trans. Vis. Comput. Graph. 2003
Computational photography and imaging › shape and reflectance estimation
shape from texture
0.012003
Texture Synthesis for 3D Shape Representation · IEEE Trans. Vis. Comput. Graph. 2003
Visualization and visual analytics › perception › visual perception
shape perception
0.012003
Texture Synthesis for 3D Shape Representation · IEEE Trans. Vis. Comput. Graph. 2003
Visual content generation and editing
texture synthesis
0.012003
Texture Synthesis for 3D Shape Representation · IEEE Trans. Vis. Comput. Graph. 2003
Geometric modeling and processing
surface parameterization
0.012003
Texture Synthesis for 3D Shape Representation · IEEE Trans. Vis. Comput. Graph. 2003

Methods — techniques the papers use, named apart from their topics

vector field · 0.0texture synthesis · 0.0seam avoidance · 0.0
YearPublicationVenuePosition
2003 Texture Synthesis for 3D Shape Representation
abstract
Considerable evidence suggests that a viewer's perception of the 3D shape of a polygonally-defined object can be significantly affected (either masked or enhanced) by the presence of a surface texture pattern. However, investigations into the specific mechanisms of texture's effect on shape perception are still ongoing and the question of how to design and apply a texture pattern to a surface in order to best facilitate shape perception remains open. Recently, we have suggested that, for anisotropic texture patterns, the accuracy of shape judgments may be significantly affected by the orientation of the surface texture pattern anisotropy with respect to the principal directions of curvature over the surface. However, it has been difficult, until this time, to conduct controlled studies specifically investigating the effect of texture orientation on shape perception because there has been no simple and reliable method for texturing an arbitrary doubly curved surface with a specified input pattern such that the dominant orientation of the pattern everywhere follows a predefined directional vector field over the surface, while seams and projective distortion of the pattern are avoided. In this paper, we present a straightforward and highly efficient method for achieving such a texture and describe how it can potentially be used to enhance shape representation. Specifically, we describe a novel, efficient, automatic algorithm for seamlessly synthesizing, from a sample 2D pattern, a high resolution fitted surface texture in which the dominant orientation of the pattern locally follows a specified vector field over the surface at a per-pixel level and in which seams, projective distortion, and repetition artifacts in the texture pattern are nearly completely avoided. We demonstrate the robustness of our method with a variety of texture swatches applied to standard graphics data sets and we explain how our method can be used to facilitate research in the perception of shape from texture.
Gabriele Gorla, Victoria Interrante, Guillermo Sapiro
IEEE Trans. Vis. Comput. Graph.1