Michael Oechsle

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 662 researchers and is trusted by 743 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 227 citations into it; #1,589 of 21,575 by trust here, #1,762 by citations
• 3D Vision & Pose — 133 citations into it; #77 of 689 by trust here, #37 by citations
• Computational Mechanics — 130 citations into it; #246 of 10,001 by trust here, #158 by citations
• Computer Graphics and Computer-Aided Design — 96 citations into it; #39 of 1,584 by trust here, #32 by citations
• Generative Models — 56 citations into it; #1,789 of 1,956 by trust here, #1,200 by citations
• Aerospace Engineering — 42 citations into it
• Geology — 29 citations into it
• Artificial Intelligence — 20 citations into it

Whom Michael Oechsle trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Andreas Geiger
6. Yann LeCun
7. Yoshua Bengio
8. Ross B. Girshick
9. Lars M. Mescheder
10. Danilo Jimenez Rezende

Who trusts Michael Oechsle overall

1. Songyou Peng
2. Michael Niemeyer
3. Andreas Geiger
4. Zehao Yu
5. Eric Chan
6. Lior Yariv
7. Zerong Zheng
8. Matthew Tancik
9. Anpei Chen
10. Jonathan T. Barron

Papers from Michael Oechsle's perspective

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

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

Their papers

• Occupancy Networks: Learning 3D Reconstruction in Function Space (2018) — 3,446 citations
• Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision (2019) — 1,118 citations
• Texture Fields: Learning Texture Representations in Function Space (2019) — 336 citations
• UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction (2021) — 907 citations
• Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics (2019) — 317 citations