Wojciech Matusik

Computer Vision and Pattern Recognition · 2000–2021

A researcher in the Science corpus: 18 papers; trusts 385 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 200 citations into it; #5,423 of 21,575 by trust here, #4,926 by citations
• Computer Graphics and Computer-Aided Design — 61 citations into it; #194 of 1,584 by trust here, #188 by citations
• Artificial Intelligence — 40 citations into it
• Computational Mechanics — 35 citations into it; #1,280 of 10,001 by trust here, #1,368 by citations
• Human-Computer Interaction — 32 citations into it; #2,132 of 2,253 by trust here, #914 by citations
• Media Technology — 24 citations into it
• Aerospace Engineering — 21 citations into it
• Control and Systems Engineering — 20 citations into it; #2,934 of 8,257 by trust here, #1,718 by citations

Whom Wojciech Matusik trusts overall

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

1. Charles M. Bishop
2. Ian T. Jolliffe
3. S. M. Seitz
4. Daniel D. Lee
5. Richard Szeliski
6. Henrik Wann Jensen
7. Geoffrey E. Hinton
8. Kaiming He
9. Alex Krizhevsky
10. Brian Curless

Who trusts Wojciech Matusik overall

1. Aaron Hertzmann
2. Sean Bell
3. Dan B. Goldman
4. Nicolas Bonneel
5. Joel Carranza
6. Christian Theobalt
7. Markus H. Gross
8. Zerong Zheng
9. S. M. Seitz
10. Kristen Grauman

Papers from Wojciech Matusik's perspective

Ordered by how strongly the researchers Wojciech Matusik 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Image-based visual hulls (2000) — 1,086 citations
• Gaze360: Physically Unconstrained Gaze Estimation in the Wild (2019) — 494 citations
• CG2Real: Improving the Realism of Computer Generated Images Using a Large Collection of Photographs (2011) — 118 citations
• A data-driven reflectance model (2003) — 1,010 citations
• Dynamic shape capture using multi-view photometric stereo (2009) — 322 citations
• 3D TV: a scalable system for real-time acquisition, transmission, and autostereoscopic display of dynamic scenes (2004) — 575 citations
• Polyhedral Visual Hulls for Real-Time Rendering (2001) — 341 citations
• Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks (2018) — 153 citations
• Learning the signatures of the human grasp using a scalable tactile glove (2019) — 1,066 citations
• Practical motion capture in everyday surroundings (2007) — 430 citations
• Towards real-time photorealistic 3D holography with deep neural networks (2021) — 491 citations
• Learning human–environment interactions using conformal tactile textiles (2021) — 378 citations
• Learning-based Video Motion Magnification (2018) — 193 citations
• ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics (2018) — 318 citations
• Eye Tracking for Everyone (2016) — 997 citations
• Articulated mesh animation from multi-view silhouettes (2008) — 790 citations
• Analysis of human faces using a measurement-based skin reflectance model (2006) — 344 citations
• Experimental analysis of BRDF models (2005) — 603 citations