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

Pol Banzet

dblp:248/9032 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1

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
Rendering · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering › appearance acquisition › material acquisition
material parameter estimation
0.412019
Real2Sim: visco-elastic parameter estimation from dynamic motion · ACM Trans. Graph. 2019

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

gradient-based optimization · 0.4direct sensitivity analysis · 0.4adjoint state method · 0.4
YearPublicationVenuePosition
2019 Real2Sim: visco-elastic parameter estimation from dynamic motion
abstract
This paper presents a method for optimizing visco-elastic material parameters of a finite element simulation to best approximate the dynamic motion of real-world soft objects. We compute the gradient with respect to the material parameters of a least-squares error objective function using either direct sensitivity analysis or an adjoint state method. We then optimize the material parameters such that the simulated motion matches real-world observations as closely as possible. In this way, we can directly build a useful simulation model that captures the visco-elastic behaviour of the specimen of interest. We demonstrate the effectiveness of our method on various examples such as numerical coarsening, custom-designed objective functions, and of course real-world flexible elastic objects made of foam or 3D printed lattice structures, including a demo application in soft robotics.
David Hahn, Pol Banzet, James M. Bern, Stelian Coros
ACM Trans. Graph.2