Results 231 to 240 of about 6,317 (306)

Hierarchical Optimization of the As‐Rigid‐As‐Possible Energy

open access: yesComputer Graphics Forum, EarlyView.
Abstract The As‐Rigid‐As‐Possible (ARAP) energy [SA07] has become a versatile ingredient in various geometry processing and machine learning methods. The classic method for its minimization is a block coordinate descent, alternating between local rotation estimation and a global linear solve, which converges slowly for large problem instances.
Hendrik Meyer, Bernd Bickel, Marc Alexa
wiley   +1 more source

Quantitative Metrics for Edge Bundling of Network Visualizations

open access: yesComputer Graphics Forum, EarlyView.
Abstract Edge bundling is widely used for reducing visual clutter in large 2D network and trajectory visualizations. Various edge bundling methods have been proposed, each producing qualitatively distinct outputs for the same data; however, few quantitative metrics exist for systematic evaluation. In this paper, we propose a set of quantitative metrics
M. Wallinger   +3 more
wiley   +1 more source

The Truth, the Whole Truth, and Nothing but the Truth: Automatic Visualization Evaluation from Reconstruction Quality

open access: yesComputer Graphics Forum, EarlyView.
Abstract Recent advances in AI enable the automatic generation of visualizations directly from textual prompts using agentic workflows. However, visualizations produced via one‐shot generative methods often suffer from insufficient quality, typically requiring a human in the loop to refine the outputs.
Roxana Bujack   +4 more
wiley   +1 more source

Hirota, Fay and geometry. [PDF]

open access: yesLett Math Phys
Eynard B, Oukassi S.
europepmc   +1 more source

Class Angular Distortion Index for Dimensionality Reduction

open access: yesComputer Graphics Forum, EarlyView.
Abstract Dimensionality reduction (DR) techniques are often characterized by whether they preserve global, high‐level structures in the data or local, neighborhood structures. This distinction matters in visualization: global methods can obscure clusters while local methods can over‐emphasize them.
Kaviru Gunaratne   +2 more
wiley   +1 more source

Scalable Computation of Topological Abstractions for Scalar Data

open access: yesComputer Graphics Forum, EarlyView.
Abstract Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial ...
M. Will   +6 more
wiley   +1 more source

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