Michael Zollhoefer

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 485 researchers and is trusted by 384 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 202 citations into it; #2,109 of 21,575 by trust here, #3,765 by citations
• Computer Graphics and Computer-Aided Design — 93 citations into it
• Computational Mechanics — 90 citations into it
• 3D Vision & Pose — 56 citations into it; #100 of 689 by trust here, #136 by citations
• Generative Models — 38 citations into it
• Artificial Intelligence — 14 citations into it
• Model Architectures — 10 citations into it
• Control and Systems Engineering — 6 citations into it

Whom Michael Zollhoefer trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Jimmy Ba
6. Yoshua Bengio
7. Yann LeCun
8. Ross B. Girshick
9. Sepp Hochreiter
10. Ilya Sutskever

Who trusts Michael Zollhoefer overall

1. Vincent Sitzmann
2. Michael Niemeyer
3. Geoffrey E. Hinton
4. Matthew Tancik
5. Eric Chan
6. Alex Yu
7. Anpei Chen
8. Jonathan T. Barron
9. Pratul P. Srinivasan
10. Songyou Peng

Papers from Michael Zollhoefer's perspective

Ordered by how strongly the researchers Michael Zollhoefer 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. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations (2019) — 1,421 citations
• Mixture of volumetric primitives for efficient neural rendering (2021) — 370 citations
• A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose (2021) — 311 citations
• Neural 3D Video Synthesis from Multi-view Video (2021) — 473 citations
• Pixel-aligned Volumetric Avatars (2021) — 52 citations