- Mathematics of Deep Learning - Rene Vidal, Joan Bruna, Raja Giryes
- Geometric deep learning on graphs and manifolds - Prof. Michael Bronstein, Prof. Joan Bruna, Prof. Arthur Szlam, Prof. Xavier Bresson, Prof. Yann LeCun
- Deep and geometry (3D)
- Visual Understanding for Interaction
- Embedded Vision
- Visual Odometry & Computer Vision Applications Based on Location Clues
- Deep Learning for Robotic Vision
- Convolutional Neural Network Architecture for Geometric Matching - Ignacio Rocco, Relja Arandjelović, Josef Sivic (2704)
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation - Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas
- Global Hypothesis Generation for 6D Object Pose Estimation - Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother (147)
- A Practical Method for Fully Automatic Intrinsic Camera Calibration Using Directionally Encoded Light - Mahdi Abbaspour Tehrani, Thabo Beeler, Anselm Grundhöfer (308)
- Dynamic Time-Of-Flight - Michael Schober, Amit Adam, Omer Yair, Shai Mazor, Sebastian Nowozin (2685)
- Semantic Scene Completion From a Single Depth Image - Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, Thomas Funkhouser
- 3DMatch: Learning Local Geometric Descriptors From RGB-D Reconstructions - Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, Thomas Funkhouser
- On-The-Fly Adaptation of Regression Forests for Online Camera Relocalisation - Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Luigi Di Stefano, Philip H. S. Torr
- Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data - Joel Janai, Fatma Güney, Jonas Wulff, Michael J. Black, Andreas Geiger
James J. DiCarlo (Institute for Brain Research, MIT.) - The Science of Natural intelligence (NI): Reverse Engineering Primate Visual Perception
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Semi-Supervised Deep Learning for Monocular Depth Map Prediction Yevhen Kuznietsov, Jörg Stückler, Bastian Leib
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Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu (best paper)
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Densely Connected Convolutional Networks Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger (best paper)
- Unrolling the Shutter: CNN to Correct Motion Distortions - Vijay Rengarajan, Yogesh Balaji, A. N. Rajagopalan
- Light Field Blind Motion Deblurring - Pratul P. Srinivasan, Ren Ng, Ravi Ramamoorthi
- HSfM: Hybrid Structure-from-Motion - Hainan Cui, Xiang Gao, Shuhan Shen, Zhanyi Hu
- Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures - Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
- A New Rank Constraint on Multi-View Fundamental Matrices, and Its Application to Camera Location Recovery - Soumyadip Sengupta, Tal Amir, Meirav Galun, Tom Goldstein, David W. Jacobs, Amit Singer, Ronen Basri
- Noise Robust Depth From Focus Using a Ring Difference Filter - Jaeheung Surh, Hae-Gon Jeon, Yunwon Park, Sunghoon Im, Hyowon Ha, In So Kweon
- Group-Wise Point-Set Registration Based on Rényi's Second Order Entropy - Luis G. Sanchez Giraldo, Erion Hasanbelliu, Murali Rao, Jose C. Principe
- A Point Set Generation Network for 3D Object Reconstruction From a Single Image - Haoqiang Fan, Hao Su, Leonidas J. Guibas
- 3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder - Gil Elbaz, Tamar Avraham, Anath Fischer
- DSAC - Differentiable RANSAC for Camera Localization - Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother
Harry Shum (Artificial Intelligence and Research Group, Microsoft) - Commercializing computer vision: Success stories and lessons learned
- Local Binary Convolutional Neural Networks - Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides
- Deep Self-Taught Learning for Weakly Supervised Object Localization - Zequn Jie, Yunchao Wei, Xiaojie Jin, Jiashi Feng, Wei Liu
- Global Optimality in Neural Network Training - Benjamin D. Haeffele, René Vidal
- YOLO9000: Better, Faster, Stronger - Joseph Redmon, Ali Farhadi
- UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems - Sean Ryan Fanello, Julien Valentin, Christoph Rhemann, Adarsh Kowdle, Vladimir Tankovich, Philip Davidson, Shahram Izadi
- Geometric Loss Functions for Camera Pose Regression With Deep Learning - Alex Kendall, Roberto Cipolla
- CNN-SLAM: Real-Time Dense Monocular SLAM With Learned Depth Prediction - Keisuke Tateno, Federico Tombari, Iro Laina, Nassir Navab
- Unsupervised Monocular Depth Estimation With Left-Right Consistency - Clément Godard, Oisin Mac Aodha, Gabriel J. Brostow
- Unsupervised Learning of Depth and Ego-Motion From Video - Tinghui Zhou, Matthew Brown, Noah Snavely, David G. Lowe
- Improved Stereo Matching With Constant Highway Networks and Reflective Confidence Learning - Amit Shaked, Lior Wolf
Dan Jurafsky (Computer Science, Stanford ) - Extracting Social Meaning from Language
- Geometric and Semantic 3D Reconstruction - Christian Häne, Jakob Engel, Srikumar Ramalingam, Sudipta Sinha