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.
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