H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 36 Citations 8,264 114 World Ranking 5724 National Ranking 273

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Computer graphics

Matthias Nießner focuses on Artificial intelligence, Computer vision, Computer graphics, RGB color model and Face. His research investigates the connection with Artificial intelligence and areas like State which intersect with concerns in Computing Methodologies. His work on Tracking and Segmentation as part of general Computer vision research is often related to Matching and Scale, thus linking different fields of science.

The Computer graphics study combines topics in areas such as Depth map and View synthesis, Rendering. Matthias Nießner undertakes multidisciplinary investigations into RGB color model and Context in his work. His Face study combines topics in areas such as Monocular and Facial expression.

His most cited work include:

  • Real-time 3D reconstruction at scale using voxel hashing (566 citations)
  • ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes (461 citations)
  • BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration (333 citations)

What are the main themes of his work throughout his whole career to date?

Matthias Nießner mostly deals with Artificial intelligence, Computer vision, RGB color model, Computer graphics and Rendering. His research investigates the link between Artificial intelligence and topics such as Pattern recognition that cross with problems in Margin. His Computer vision research includes elements of Facial expression and Benchmark.

The study incorporates disciplines such as Tracking and Segmentation in addition to RGB color model. His work investigates the relationship between Computer graphics and topics such as View synthesis that intersect with problems in Leverage. Matthias Nießner studied Rendering and Subdivision surface that intersect with Tessellation.

He most often published in these fields:

  • Artificial intelligence (75.19%)
  • Computer vision (60.15%)
  • RGB color model (34.59%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (75.19%)
  • Computer vision (60.15%)
  • RGB color model (34.59%)

In recent papers he was focusing on the following fields of study:

Matthias Nießner spends much of his time researching Artificial intelligence, Computer vision, RGB color model, Rendering and Object. In Artificial intelligence, Matthias Nießner works on issues like Pattern recognition, which are connected to Transfer of learning. His study in the field of Real image and Contrast also crosses realms of Key and CAD.

His RGB color model research incorporates elements of Tracking and Virtual reality. His research integrates issues of Image synthesis and Computer graphics in his study of Rendering. His Object study incorporates themes from Contrast and Task.

Between 2019 and 2021, his most popular works were:

  • State of the Art on Neural Rendering (54 citations)
  • Local Implicit Grid Representations for 3D Scenes (31 citations)
  • Image-guided Neural Object Rendering (17 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary areas of investigation include Artificial intelligence, Computer vision, Rendering, Computer graphics and Segmentation. Matthias Nießner combines subjects such as Polygon mesh and Pattern recognition with his study of Artificial intelligence. His work on Object and RGB color model as part of general Computer vision study is frequently linked to Key and CAD, bridging the gap between disciplines.

Matthias Nießner has included themes like Artificial neural network and Digital reproduction in his RGB color model study. His Computer graphics research focuses on subjects like Metaverse, which are linked to Image synthesis. His Segmentation study which covers Point cloud that intersects with Information retrieval and Point.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Real-time 3D reconstruction at scale using voxel hashing

Matthias Nießner;Michael Zollhöfer;Shahram Izadi;Marc Stamminger.
international conference on computer graphics and interactive techniques (2013)

598 Citations

ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes

Angela Dai;Angel X. Chang;Manolis Savva;Maciej Halber.
arXiv: Computer Vision and Pattern Recognition (2017)

470 Citations

Deferred neural rendering: image synthesis using neural textures

Justus Thies;Michael Zollhöfer;Matthias Nießner.
ACM Transactions on Graphics (2019)

409 Citations

BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration

Angela Dai;Matthias Nießner;Michael Zollhöfer;Shahram Izadi.
ACM Transactions on Graphics (2017)

344 Citations

Real-time non-rigid reconstruction using an RGB-D camera

Michael Zollhöfer;Matthias Nießner;Shahram Izadi;Christoph Rehmann.
international conference on computer graphics and interactive techniques (2014)

343 Citations

Matterport3D: Learning from RGB-D Data in Indoor Environments

Angel Chang;Angela Dai;Thomas Funkhouser;Maciej Halber.
arXiv: Computer Vision and Pattern Recognition (2017)

281 Citations

Face2Face: real-time face capture and reenactment of RGB videos

Justus Thies;Michael Zollhöfer;Marc Stamminger;Christian Theobalt.
Communications of The ACM (2018)

241 Citations

FaceForensics++: Learning to Detect Manipulated Facial Images

Andreas Rössler;Davide Cozzolino;Luisa Verdoliva;Christian Riess.
arXiv: Computer Vision and Pattern Recognition (2019)

240 Citations

Real-time expression transfer for facial reenactment

Justus Thies;Michael Zollhöfer;Matthias Nießner;Levi Valgaerts.
international conference on computer graphics and interactive techniques (2015)

203 Citations

VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction

Matthias Innmann;Michael Zollhöfer;Matthias Nießner;Christian Theobalt.
european conference on computer vision (2016)

187 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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