Jean-Emmanuel Deschaud

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 82 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 48 citations into it; #5,232 of 21,575 by trust here, #6,485 by citations
• Computational Mechanics — 41 citations into it; #7,822 of 10,001 by trust here, #1,325 by citations
• Geology — 36 citations into it; #538 of 687 by trust here, #132 by citations
• Aerospace Engineering — 28 citations into it; #699 of 6,421 by trust here, #5,172 by citations
• Environmental Engineering — 24 citations into it; #428 of 5,479 by trust here, #2,867 by citations
• Evaluation & Benchmarks — 11 citations into it
• 3D Vision & Pose — 10 citations into it
• Computer Graphics and Computer-Aided Design — 7 citations into it

Whom Jean-Emmanuel Deschaud trusts overall

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

1. Brian Curless
2. Ji Zhang
3. Marc Levoy
4. Natasha Gelfand
5. Szymon Rusinkiewicz
6. Richard A. Newcombe
7. Sanjiv Singh
8. Kok-Lim Low
9. David Levin
10. Andreas Geiger

Who trusts Jean-Emmanuel Deschaud overall

1. Martin Brossard
2. Han Wang
3. Xieyuanli Chen
4. Ignacio Vizzo
5. Liangkai Liu
6. Jens Behley
7. Axel Barrau
8. Chen Wang
9. Andres Milioto Emanuele Palazzolo
10. Tiziano Guadagnino

Papers from Jean-Emmanuel Deschaud's perspective

Ordered by how strongly the researchers Jean-Emmanuel Deschaud 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
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

• KPConv: Flexible and Deformable Convolution for Point Clouds (2019) — 3,358 citations
• IMLS-SLAM: Scan-to-Model Matching Based on 3D Data (2018) — 357 citations
• Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification (2017) — 330 citations