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.
Papers
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Proceedings ArticleDOI
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
TL;DR: This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.
Posted Content
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
TL;DR: A hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set and proposes novel set learning layers to adaptively combine features from multiple scales to learn deep point set features efficiently and robustly.
Journal ArticleDOI
The Earth Mover's Distance as a Metric for Image Retrieval
TL;DR: This paper investigates the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval, and compares the retrieval performance of the EMD with that of other distances.
Posted Content
ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang,Thomas Funkhouser,Leonidas J. Guibas,Pat Hanrahan,Qixing Huang,Zimo Li,Silvio Savarese,Manolis Savva,Shuran Song,Hao Su,Jianxiong Xiao,Li Yi,Fisher Yu +12 more
TL;DR: ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy, a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations.
Proceedings Article
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
TL;DR: PointNet++ as discussed by the authors applies PointNet recursively on a nested partitioning of the input point set to learn local features with increasing contextual scales, and proposes novel set learning layers to adaptively combine features from multiple scales.