What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer graphics
Ulrich Neumann mostly deals with Artificial intelligence, Computer vision, Augmented reality, Computer graphics and Robustness.
His study in Feature extraction, Segmentation, Point cloud, Pixel and Computer graphics is carried out as part of his Artificial intelligence studies.
His Computer vision research is multidisciplinary, incorporating perspectives in Computer facial animation and Visualization.
Ulrich Neumann has researched Augmented reality in several fields, including Calibration, Tracking system, Eye tracking and Virtual reality.
His work on Rendering, Planar and Shadow mapping as part of his general Computer graphics study is frequently connected to Opacity, thereby bridging the divide between different branches of science.
His Robustness research incorporates themes from Speech recognition and Motion capture.
His most cited work include:
- Analysis of emotion recognition using facial expressions, speech and multimodal information (617 citations)
- Expression cloning (327 citations)
- Hybrid inertial and vision tracking for augmented reality registration (292 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Computer graphics, Augmented reality and Virtual reality.
His research related to Point cloud, Feature extraction, Segmentation, Rendering and Visualization might be considered part of Artificial intelligence.
His research in Point cloud intersects with topics in Lidar and Point.
His studies in Computer vision integrate themes in fields like Computer facial animation and Computer graphics.
His research investigates the connection between Computer facial animation and topics such as Motion that intersect with problems in Speech recognition.
His Augmented reality study combines topics from a wide range of disciplines, such as Fiducial marker, Motion estimation, Image registration, Kalman filter and Robustness.
He most often published in these fields:
- Artificial intelligence (68.11%)
- Computer vision (58.66%)
- Computer graphics (22.05%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (68.11%)
- Computer vision (58.66%)
- Point cloud (10.63%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, Point cloud, Segmentation and Convolutional neural network.
His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence.
His research integrates issues of Iterative method and Representation in his study of Computer vision.
His study in Point cloud is interdisciplinary in nature, drawing from both Grid, Graph, Computational science and Feature vector.
His research in Computational science focuses on subjects like Augmented reality, which are connected to Polygon mesh.
His studies in Convolutional neural network integrate themes in fields like Function and Pruning.
Between 2015 and 2021, his most popular works were:
- SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation (255 citations)
- Recurrent Slice Networks for 3D Segmentation of Point Clouds (200 citations)
- DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction (151 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Programming language
Ulrich Neumann mainly investigates Artificial intelligence, Point cloud, Convolutional neural network, Segmentation and Recurrent neural network.
His Artificial intelligence research is multidisciplinary, relying on both Data modeling, Polygon mesh and Computer vision.
As part of his studies on Computer vision, Ulrich Neumann often connects relevant subjects like Spatial analysis.
In his research, Graph, Time complexity and Grid is intimately related to Computational science, which falls under the overarching field of Point cloud.
His Segmentation study deals with Speedup intersecting with Machine learning.
His Recurrent neural network research incorporates themes from Algorithm, Sequence and Layer.
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