L
Leonidas J. Guibas
Researcher at Stanford University
Publications - 736
Citations - 99526
Leonidas J. Guibas is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 124, co-authored 691 publications receiving 79200 citations. Previous affiliations of Leonidas J. Guibas include PARC & Association for Computing Machinery.
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Journal ArticleDOI
An efficient algorithm for finding the CSG representation of a simple polygon
TL;DR: A new proof that the interior of each simple polygon can be represented by a monotone boolean formula based on the half-planes supporting the sides of the polygon and using each such half-plane only once.
Posted Content
Learning Program Embeddings to Propagate Feedback on Student Code
TL;DR: In this paper, a neural network method is proposed to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features.
Proceedings ArticleDOI
Parametric and kinetic minimum spanning trees
TL;DR: This work considers the parametric minimum spanning tree problem, in which a graph is given a graph with edge weights that are linear functions of a parameter /spl lambda/ and wish to compute the sequence of minimum spanning trees generated as /spl Lambda/ varies.
Proceedings ArticleDOI
Computing and processing correspondences with functional maps
Maks Ovsjanikov,Etienne Corman,Michael M. Bronstein,Emanuele Rodolà,Mirela Ben-Chen,Leonidas J. Guibas,Frédéric Chazal,Alexander M. Bronstein +7 more
TL;DR: The functional approach to finding correspondences between non-rigid shapes, the design and analysis of tangent vector fields on surfaces, consistent map estimation in networks of shapes and applications to shape and image segmentation, shape variability analysis, and other areas are discussed.
Journal ArticleDOI
A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries
TL;DR: In this article, a deep learning framework for flow field prediction in irregular domains is presented, where the solution is a function of the geometry of either the domain or objects inside the domain.