H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 96 Citations 44,383 447 World Ranking 179 National Ranking 11

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Image segmentation, Segmentation and Pattern recognition. His research links Algorithm with Artificial intelligence. His studies deal with areas such as Simultaneous localization and mapping, Visual odometry and Odometry as well as Computer vision.

His Visual odometry research incorporates elements of Image resolution, Monocular, 3D reconstruction, Augmented reality and Pose. His Image segmentation study integrates concerns from other disciplines, such as Minification and Regular polygon. His research in Pattern recognition intersects with topics in Prior probability and Level set.

His most cited work include:

  • LSD-SLAM: Large-Scale Direct Monocular SLAM (1831 citations)
  • A benchmark for the evaluation of RGB-D SLAM systems (1670 citations)
  • FlowNet: Learning Optical Flow with Convolutional Networks (1644 citations)

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

Artificial intelligence, Computer vision, Algorithm, Segmentation and Pattern recognition are his primary areas of study. Artificial intelligence is closely attributed to Machine learning in his work. His research investigates the connection with Computer vision and areas like Visual odometry which intersect with concerns in Monocular.

His Algorithm study incorporates themes from Shape analysis and Mathematical optimization. His Pattern recognition research incorporates themes from Image and Prior probability. His work on Scale-space segmentation as part of general Image segmentation study is frequently connected to Initialization, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.

He most often published in these fields:

  • Artificial intelligence (66.41%)
  • Computer vision (43.25%)
  • Algorithm (22.55%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (66.41%)
  • Computer vision (43.25%)
  • Algorithm (22.55%)

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

Daniel Cremers mainly investigates Artificial intelligence, Computer vision, Algorithm, Robustness and Deep learning. The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition. His Computer vision study frequently intersects with other fields, such as Visual odometry.

His research integrates issues of Pipeline and Inverse problem in his study of Algorithm. Daniel Cremers combines subjects such as Rolling shutter and Robotics with his study of Robustness. His Deep learning research is multidisciplinary, incorporating perspectives in Robot, Inference, Iterative reconstruction and Benchmark.

Between 2017 and 2021, his most popular works were:

  • Direct Sparse Odometry (1053 citations)
  • Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (196 citations)
  • Video Object Segmentation without Temporal Information (155 citations)

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

  • Artificial intelligence
  • Computer vision
  • Statistics

Daniel Cremers mainly focuses on Artificial intelligence, Computer vision, Odometry, Robustness and Visual odometry. Daniel Cremers studied Artificial intelligence and Machine learning that intersect with Generalization. His study in Leverage extends to Computer vision with its themes.

The various areas that Daniel Cremers examines in his Odometry study include Pixel, Recurrence relation, Inertial measurement unit and Monocular. His work deals with themes such as Simultaneous localization and mapping, Rolling shutter and Bundle adjustment, which intersect with Robustness. His Visual odometry study combines topics in areas such as Ground truth and Augmented reality.

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

LSD-SLAM: Large-Scale Direct Monocular SLAM

Jakob Engel;Thomas Schöps;Daniel Cremers.
european conference on computer vision (2014)

2378 Citations

A benchmark for the evaluation of RGB-D SLAM systems

Jrgen Sturm;Nikolas Engelhard;Felix Endres;Wolfram Burgard.
intelligent robots and systems (2012)

1870 Citations

FlowNet: Learning Optical Flow with Convolutional Networks

Alexey Dosovitskiy;Philipp Fischery;Eddy Ilg;Philip Hausser.
international conference on computer vision (2015)

1508 Citations

A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape

Daniel Cremers;Mikael Rousson;Rachid Deriche.
International Journal of Computer Vision (2007)

1191 Citations

Direct Sparse Odometry

Jakob Engel;Vladlen Koltun;Daniel Cremers.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)

979 Citations

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Nikolaus Mayer;Eddy Ilg;Philip Hausser;Philipp Fischer.
computer vision and pattern recognition (2016)

960 Citations

An evaluation of the RGB-D SLAM system

Felix Endres;Jurgen Hess;Nikolas Engelhard;Jurgen Sturm.
international conference on robotics and automation (2012)

827 Citations

Dense visual SLAM for RGB-D cameras

Christian Kerl;Jurgen Sturm;Daniel Cremers.
intelligent robots and systems (2013)

674 Citations

FlowNet: Learning Optical Flow with Convolutional Networks

Philipp Fischer;Alexey Dosovitskiy;Eddy Ilg;Philip Häusser.
arXiv: Computer Vision and Pattern Recognition (2015)

654 Citations

3-D Mapping With an RGB-D Camera

Felix Endres;Jurgen Hess;Jurgen Sturm;Daniel Cremers.
IEEE Transactions on Robotics (2014)

651 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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