.png){kind=icon}
Return
Near-Regular Structure Discovery Using Linear Programming
.png){kind=icon}
PRE
AI
DOI：10.1145/2535596.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Near-regular structures are common in manmade and natural objects. Algorithmic detection of such regularity greatly facilitates our understanding of shape structures, leads to compact encoding of input geometries, and enables efficient generation and manipulation of complex patterns on both acquired and synthesized objects. Such regularity manifests itself both in the repetition of certain geometric elements, as well as in the structured arrangement of the elements. We cast the regularity detection problem as an optimization and efficiently solve it using linear programming techniques. Our optimization has a discrete aspect, that is, the connectivity relationships among the elements, as well as a continuous aspect, namely the locations of the elements of interest. Both these aspects are captured by our near-regular structure extraction framework, which alternates between discrete and continuous optimizations. We demonstrate the effectiveness of our framework on a variety of problems including near-regular structure extraction, structure-preserving pattern manipulation, and markerless correspondence detection. Robustness results with respect to geometric and topological noise are presented on synthesized, real-world, and also benchmark datasets.
Keywords:
Algorithms
Experimentation
Theory
Intrinsic near-regular structure
integer and linear programming
pattern manipulation
markerless correspondence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.5
Papers:
4.7K
Citations:
3.6W
Organization
Cited Papers
ShareSaveShareSaveAnomalous diffusion on the servosphere: A potential tool for detecting inherent organismal movement patterns.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSave
ShareSaveShareSaveShareSave
View more