What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Geometry
Leonidas J. Guibas mainly focuses on Artificial intelligence, Algorithm, Computer vision, Point cloud and Shape analysis.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
His Algorithm research includes elements of Set, Mathematical optimization, Data structure and Regular polygon.
The concepts of his Computer vision study are interwoven with issues in Interpolation, Robustness and Mobile robot.
His Point cloud study combines topics from a wide range of disciplines, such as Geometric data analysis, Feature extraction, Artificial neural network, Minimum bounding box and Voxel.
He works mostly in the field of Shape analysis, limiting it down to topics relating to Invariant and, in certain cases, Topology.
His most cited work include:
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (3318 citations)
- The Earth Mover's Distance as a Metric for Image Retrieval (3198 citations)
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2476 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Algorithm, Combinatorics, Computer vision and Point cloud.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His Algorithm research is multidisciplinary, incorporating perspectives in Mathematical optimization, Theoretical computer science, Set and Data structure.
His research in Combinatorics intersects with topics in Discrete mathematics, Regular polygon, Simple polygon, Plane and Line segment.
His study in RGB color model and Shape analysis are all subfields of Computer vision.
His Point cloud research is multidisciplinary, incorporating elements of Point and Deep learning.
He most often published in these fields:
- Artificial intelligence (34.21%)
- Algorithm (22.51%)
- Combinatorics (19.44%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (34.21%)
- Point cloud (12.87%)
- Computer vision (14.47%)
In recent papers he was focusing on the following fields of study:
Leonidas J. Guibas spends much of his time researching Artificial intelligence, Point cloud, Computer vision, Artificial neural network and Algorithm.
Leonidas J. Guibas has included themes like Machine learning and Pattern recognition in his Artificial intelligence study.
His work in Pattern recognition covers topics such as Autoencoder which are related to areas like Geometric data analysis.
His studies examine the connections between Point cloud and genetics, as well as such issues in Surface, with regards to Pixel.
His research investigates the connection with Algorithm and areas like Polygon mesh which intersect with concerns in Invariant.
His research integrates issues of Shape analysis and Computer graphics in his study of Deep learning.
Between 2017 and 2021, his most popular works were:
- Frustum PointNets for 3D Object Detection from RGB-D Data (867 citations)
- Taskonomy: Disentangling Task Transfer Learning (385 citations)
- Learning Representations and Generative Models for 3D Point Clouds (337 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Geometry
His scientific interests lie mostly in Artificial intelligence, Point cloud, Deep learning, Algorithm and Segmentation.
His work deals with themes such as Machine learning, Computer vision and Pattern recognition, which intersect with Artificial intelligence.
His Point cloud study integrates concerns from other disciplines, such as Training set, Minimum bounding box, Feature learning, Generalization and Robustness.
His Deep learning research is multidisciplinary, relying on both Adversarial system, Graph, SAFER, Euclidean geometry and Variety.
Leonidas J. Guibas interconnects Equivalence, Object matching, Surface and Invariant in the investigation of issues within Algorithm.
His work carried out in the field of Segmentation brings together such families of science as Motion, Orientation, Articulation, Convolution and Shape analysis.
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