What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Geometry
His primary areas of investigation include Artificial intelligence, Computer vision, Algorithm, Polygon mesh and Topology.
His work carried out in the field of Artificial intelligence brings together such families of science as Surface reconstruction and Pattern recognition.
Daniel Cohen-Or conducts interdisciplinary study in the fields of Computer vision and Process through his research.
His Algorithm research incorporates elements of Point cloud, Outlier, Mathematical optimization, Point and Noise reduction.
His work deals with themes such as Theoretical computer science, Mesh generation, T-vertices, Combinatorics and Position, which intersect with Polygon mesh.
His Topology study integrates concerns from other disciplines, such as Linear system, Rigid transformation, Interpolation, Surface and Invariant.
His most cited work include:
- Laplacian surface editing (871 citations)
- Computing and rendering point set surfaces (708 citations)
- Deep photo: model-based photograph enhancement and viewing (586 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Computer graphics.
His Artificial intelligence study focuses mostly on Image, Segmentation, Object, Feature and Rendering.
Many of his studies on Computer vision involve topics that are commonly interrelated, such as Computer graphics.
His Algorithm study combines topics from a wide range of disciplines, such as Point cloud and Polygon mesh.
Topology is closely connected to Surface in his research, which is encompassed under the umbrella topic of Polygon mesh.
His Pattern recognition study frequently links to other fields, such as Artificial neural network.
He most often published in these fields:
- Artificial intelligence (56.96%)
- Computer vision (40.21%)
- Algorithm (14.95%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (56.96%)
- Computer vision (40.21%)
- Algorithm (14.95%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Point cloud.
As a part of the same scientific family, Daniel Cohen-Or mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Generator.
His research on Computer vision often connects related areas such as Grid.
The concepts of his Algorithm study are interwoven with issues in Monocular, Forward kinematics, Motion, Convolution and Convolutional neural network.
He interconnects Test data generation, Leverage, Embedding, Object and Visualization in the investigation of issues within Pattern recognition.
The study incorporates disciplines such as Graph, Polygon mesh, Noise, Surface and Point in addition to Point cloud.
Between 2018 and 2021, his most popular works were:
- MeshCNN: a network with an edge (150 citations)
- PU-GAN: A Point Cloud Upsampling Adversarial Network (64 citations)
- SAGNet: structure-aware generative network for 3D-shape modeling (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Geometry
Daniel Cohen-Or mainly investigates Artificial intelligence, Pattern recognition, Image, Artificial neural network and Embedding.
His Artificial intelligence research includes themes of Sequence and Computer vision.
His Computer vision research integrates issues from Motion, Differentiable function and Forward kinematics.
His biological study deals with issues like Object, which deal with fields such as Segmentation, Metric and Construct.
His Similarity and Texture transfer study in the realm of Image interacts with subjects such as Code.
Daniel Cohen-Or works mostly in the field of Feature, limiting it down to topics relating to Upsampling and, in certain cases, Algorithm and Point cloud, as a part of the same area of interest.
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