V
Vladimir G. Kim
Researcher at Adobe Systems
Publications - 114
Citations - 7588
Vladimir G. Kim is an academic researcher from Adobe Systems. The author has contributed to research in topics: Shape analysis (digital geometry) & Point cloud. The author has an hindex of 32, co-authored 111 publications receiving 5575 citations. Previous affiliations of Vladimir G. Kim include Stanford University & Carnegie Mellon University.
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Journal ArticleDOI
A scalable active framework for region annotation in 3D shape collections
Li Yi,Vladimir G. Kim,Duygu Ceylan,I-Chao Shen,Mengyan Yan,Hao Su,Cewu Lu,Qixing Huang,Alla Sheffer,Leonidas J. Guibas +9 more
TL;DR: This work proposes a novel active learning method capable of enriching massive geometric datasets with accurate semantic region annotations, and demonstrates that incorporating verification of all produced labelings within this unified objective improves both accuracy and efficiency of the active learning procedure.
Proceedings ArticleDOI
Shape-based recognition of 3D point clouds in urban environments
TL;DR: This paper quantitatively evaluate the design of a system for recognizing objects in 3D point clouds of urban environments and tradeoffs of different alternatives in a truthed part of a scan of Ottawa that contains approximately 100 million points and 1000 objects of interest.
Proceedings ArticleDOI
A Papier-Mache Approach to Learning 3D Surface Generation
TL;DR: This work introduces a method for learning to generate the surface of 3D shapes as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape.
Proceedings ArticleDOI
Blended intrinsic maps
TL;DR: This paper describes a fully automatic pipeline for finding an intrinsic map between two non-isometric, genus zero surfaces and solves a global optimization problem that selects candidate maps based both on their area preservation and consistency with other selected maps.
Proceedings Article
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
TL;DR: A method for learning to generate the surface of 3D shapes as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape.