What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer graphics
Artificial intelligence, Computer vision, Computer graphics, Rendering and Texture filtering are his primary areas of study.
Artificial intelligence is closely attributed to Pattern recognition in his work.
His research in Computer vision intersects with topics in Structure and Stereo display.
His Computer graphics research incorporates elements of Stereoscopy and Polygon.
His Rendering research is multidisciplinary, incorporating elements of Computational geometry, Computer graphics, Texture mapping and Graphics.
His studies in Texture filtering integrate themes in fields like Graphics pipeline, Graphics hardware and Surfel.
His most cited work include:
- Surfels: surface elements as rendering primitives (709 citations)
- Surface splatting (470 citations)
- Pointshop 3D: an interactive system for point-based surface editing (294 citations)
What are the main themes of his work throughout his whole career to date?
Matthias Zwicker spends much of his time researching Artificial intelligence, Computer vision, Rendering, Computer graphics and Pattern recognition.
As part of the same scientific family, Matthias Zwicker usually focuses on Artificial intelligence, concentrating on Machine learning and intersecting with Representation.
Matthias Zwicker has included themes like Surface and Stereo display in his Computer vision study.
His Rendering research is multidisciplinary, relying on both Algorithm and Graphics.
He interconnects Image resolution, Visualization and Polygon in the investigation of issues within Computer graphics.
The various areas that he examines in his Pattern recognition study include Autoencoder and Image restoration.
He most often published in these fields:
- Artificial intelligence (67.46%)
- Computer vision (45.45%)
- Rendering (24.88%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (67.46%)
- Point cloud (11.48%)
- Deep learning (8.13%)
In recent papers he was focusing on the following fields of study:
Matthias Zwicker mostly deals with Artificial intelligence, Point cloud, Deep learning, Discriminative model and Artificial neural network.
His Artificial intelligence study combines topics in areas such as Machine learning, Computer vision and Pattern recognition.
His work deals with themes such as Generator and Computer graphics, which intersect with Computer vision.
His study looks at the relationship between Point cloud and topics such as Image, which overlap with Regularization, Consistency and Inference.
His research integrates issues of Segmentation, Aggregate, Encoding and Sequence in his study of Discriminative model.
His Artificial neural network research also works with subjects such as
- Algorithm, which have a strong connection to Rendering, Voxel and Iterative reconstruction,
- Leverage, which have a strong connection to Feature vector, Sample space and Black box.
Between 2018 and 2021, his most popular works were:
- Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network (97 citations)
- SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention (76 citations)
- View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions (54 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Matthias Zwicker mainly focuses on Artificial intelligence, Deep learning, Point cloud, Discriminative model and Machine learning.
His Pattern recognition research extends to the thematically linked field of Artificial intelligence.
Matthias Zwicker combines subjects such as Voxel, Autoencoder and Closed captioning with his study of Pattern recognition.
His Point cloud research entails a greater understanding of Computer vision.
His Computer vision research includes elements of Polygon mesh and Shading.
His work in Machine learning tackles topics such as Segmentation which are related to areas like Contrast, Representation and Sequence learning.
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