Jörg Stückler

Computer Vision and Pattern Recognition · 2010–2019

A researcher in the Science corpus: 7 papers; trusts 497 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 118 citations into it; #4,297 of 21,575 by trust here, #5,680 by citations
• Aerospace Engineering — 75 citations into it; #500 of 6,421 by trust here, #859 by citations
• Geology — 22 citations into it; #469 of 687 by trust here, #586 by citations
• Media Technology — 17 citations into it
• 3D Vision & Pose — 15 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Artificial Intelligence — 12 citations into it
• Robotics & Control — 11 citations into it

Whom Jörg Stückler trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Jakob J. Engel
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Jürgen Sturm
6. Diederik P. Kingma
7. Yoshua Bengio
8. Ross B. Girshick
9. Daniel Cremers
10. Ashutosh Saxena

Who trusts Jörg Stückler overall

1. Nan Yang
2. Xinjing Cheng
3. Rui Wang
4. Adam Kraft
5. Guoquan Huang
6. Daniel Cremers
7. Zachary Teed
8. Raul Mur-Artal
9. Peng Wang
10. Amir Atapour-Abarghouei

Papers from Jörg Stückler's perspective

Ordered by how strongly the researchers Jörg Stückler trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet Large Scale Visual Recognition Challenge
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Semi-Supervised Deep Learning for Monocular Depth Map Prediction (2017) — 693 citations
• Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (2018) — 356 citations
• Direct visual-inertial odometry with stereo cameras (2016) — 228 citations
• Large-scale direct SLAM with stereo cameras (2015) — 603 citations
• EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association (2019) — 89 citations
• The TUM VI Benchmark for Evaluating Visual-Inertial Odometry (2018) — 479 citations
• Learning Motion Skills from Expert Demonstrations and Own Experience using Gaussian Process Regression (2010) — 16 citations