Shotton, Jamie

The multimodal brain tumor image segmentation benchmark (BRATS)

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 6,781

KinectFusion: Real-time dense surface mapping and tracking

Newcombe, R. A., Fitzgibbon, A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohi, P., Shotton, J., & Hodges, S. (2011) · 2011 10th IEEE International Symposium on Mixed and Augmented Reality

Robotics and Sensor-Based Localization3D Shape Modeling and Analysis

Cited by 4,008

Segmentation and Recognition Using Structure from Motion Point Clouds

Brostow, G. J., Shotton, J., Fauqueur, J., & Cipolla, R. (2008) · Lecture Notes in Computer Science

Advanced Vision and ImagingAdvanced Neural Network Applications

Cited by 1,050

Accurate, Robust, and Flexible Real-time Hand Tracking

Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Kim, D., Rhemann, C., Leichter, I., Vinnikov, A., Wei, Y., et al. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Hand Gesture Recognition SystemsHuman Pose and Action Recognition

Cited by 431

Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR

Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O. M., Das, T., Jena, R., & Price, S. J. (2012) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 276

ChaLearn Looking at People Challenge 2014: Dataset and Results

Escalera, S., Baró, X., Gonzàlez, J., Bautista, M. A., Madadi, M., Reyes, M., Ponce-López, V., Escalante, H. J., Shotton, J., & Guyon, I. (2015) · Lecture Notes in Computer Science

Human Pose and Action RecognitionHand Gesture Recognition Systems

Cited by 211

Exploiting uncertainty in regression forests for accurate camera relocalization

Valentin, J., Niebner, M., Shotton, J., Fitzgibbon, A., Izadi, S., & Torr, P. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Robotics and Sensor-Based LocalizationAdvanced Vision and Imaging

Cited by 156

GeoF: Geodesic Forests for Learning Coupled Predictors

Kontschieder, P., Kohli, P., Shotton, J., & Criminisi, A. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition

Advanced Neural Network ApplicationsMedical Image Segmentation Techniques

Cited by 63

GAIA-1: A Generative World Model for Autonomous Driving

Hu, A., Russell, L., Yeo, H., Murez, Z., Fedoseev, G., Kendall, A., Shotton, J., & Corrado, G. (2023) · arXiv

Autonomous Vehicle Technology and SafetyReinforcement Learning in Robotics

Cited by 28

GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving

Russell, L., Hu, A., Bertoni, L., Fedoseev, G., Shotton, J., Arani, E., & Corrado, G. (2025) · arXiv

Generative Adversarial Networks and Image SynthesisAutonomous Vehicle Technology and Safety

Cited by 1