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

Mayank Singh 0003

dblp:96/4770-3 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · unresolved

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

Graphics, computer vision, multimedia, augmented reality and games · 2 · 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
Geometric modeling and processing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
mesh processing
0.112010
Triangle surfaces with discrete equivalence classes · ACM Trans. Graph. 2010
Geometric modeling and processing › shape optimization
surface optimization
0.112010
Triangle surfaces with discrete equivalence classes · ACM Trans. Graph. 2010

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

poisson equation · 0.1nonlinear optimization · 0.1clustering · 0.1
YearPublicationVenuePosition
2010 Triangle surfaces with discrete equivalence classes
abstract
We propose a technique that takes a triangulated surface as input and outputs a surface with the same topology but altered geometry such that each polygon falls into a set of discrete equivalence classes. We begin by describing an error function that measures how close the polygons are to satisfying this criteria. To optimize this error function, we first cluster triangles into discrete sets such that the assignment of sets minimizes our error. We then find canonical polygons for each set using nonlinear optimization. Next, we solve a Poisson equation to find positions of vertices such that the surface polygons match the canonical polygons as close as possible. We also describe how to incorporate a fairness criteria into the optimization to avoid oscillations of the surface. We iterate this entire process until we reach a user specified tolerance, possibly adding clusters during iteration to guarantee convergence. We have been able to successfully reduce the number of unique triangles to lie within a small percentage of the total number of triangles in the surface and demonstrate our technique on various examples.
Mayank Singh 0003, Scott Schaefer
ACM Trans. Graph.1
2007 Line Drawing as a Dynamic Process
abstract
We introduce a novel mechanism for creating line drawings from three-dimensional models, which captures the dynamic nature of the drawing process. The approach takes into account the interaction between the moving human hand and the drawing instrument. This is demonstrated as applied to the specific problem of making silhouette drawings from polygonal models. A control system drives a pen by tracking the contour of the polygonal model as projected onto the drawing surface, thus mimicking hand motion. The pen is treated as a physically-based object with momentum, giving the generated lines a smooth hand-drawn quality. Lines are rendered using a ribbon metaphor, where thickness is determined by the twist of the ribbon. The twist angle can be dependent upon various attributes such as perspective depth, the curvature of the line, and the lighting of the model. A number of examples are presented, ranging from tightly controlled drawings to expressive gestural drawings.
Donald H. House, Mayank Singh 0003
PG2