Olaf Kähler

Computer Vision and Pattern Recognition · 2015–2016

A researcher in the Science corpus: 3 papers; trusts 161 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 56 citations into it; #18,444 of 21,575 by trust here, #8,831 by citations
• Aerospace Engineering — 49 citations into it; #5,260 of 6,421 by trust here, #1,665 by citations
• Geology — 17 citations into it
• 3D Vision & Pose — 15 citations into it
• Robotics & Control — 9 citations into it
• Computer Graphics and Computer-Aided Design — 6 citations into it; #1,553 of 1,584 by trust here, #843 by citations
• Environmental Engineering — 5 citations into it

Whom Olaf Kähler trusts overall

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

1. Richard A. Newcombe
2. Thomas Whelan
3. Andrea Vedaldi
4. Shahram Izadi
5. Andrew Zisserman
6. Timothy A. Davis
7. Jürgen Sturm
8. Frank Steinbrücker
9. Andrew J. Davison
10. Matthias Nießner

Who trusts Olaf Kähler overall

1. Angela Dai
2. Marcus Gualtieri
3. Andreas ten Pas
4. Xingrui Yang
5. Michael Firman
6. Matthias Nießner
7. Christopher Bongsoo Choy
8. Hai Li
9. Kate Saenko
10. Victor Adrian Prisacariu

Papers from Olaf Kähler's perspective

Ordered by how strongly the researchers Olaf Kähler 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction (2015) — 213 citations
• Real-Time Large-Scale Dense 3D Reconstruction with Loop Closure (2016) — 134 citations
• Very High Frame Rate Volumetric Integration of Depth Images on Mobile Devices (2015) — 322 citations