Christian Kerl

Computer Vision and Pattern Recognition · 2013

A researcher in the Science corpus: 3 papers; trusts 52 researchers and is trusted by 70 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it; #3,393 of 21,575 by trust here, #6,089 by citations
• Aerospace Engineering — 35 citations into it; #232 of 6,421 by trust here, #965 by citations
• Geology — 15 citations into it; #683 of 687 by trust here, #488 by citations
• 3D Vision & Pose — 7 citations into it
• Robotics & Control — 6 citations into it

Whom Christian Kerl trusts overall

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

1. Jürgen Sturm
2. Richard A. Newcombe
3. Felix Endres
4. Frank Steinbrücker
5. Daniel Cremers
6. Peter Henry
7. Thomas Whelan
8. Nikolas Engelhard
9. Georg S. W. Klein
10. Hauke Strasdat

Who trusts Christian Kerl overall

1. Daniel Cremers
2. Jakob J. Engel
3. Thomas Whelan
4. Rui Wang
5. Sungjoon Choi
6. Takafumi Taketomi
7. Frank Steinbrücker
8. Qian-Yi Zhou
9. Tong Qin
10. Christian Forster

Papers from Christian Kerl's perspective

Ordered by how strongly the researchers Christian Kerl 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. A volumetric method for building complex models from range images
4. KinectFusion: Real-time dense surface mapping and tracking
5. A Method for Registration of 3-D Shapes
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Distinctive Image Features from Scale-Invariant Keypoints
8. RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments (International Symposium on Experimental Robotics)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Dense visual SLAM for RGB-D cameras (2013) — 985 citations
• Robust odometry estimation for RGB-D cameras (2013) — 610 citations
• Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences (2013) — 184 citations