We develop Deep Learning methods for 3D pose estimation from simple images. More precisely, our methods can estimate the 3D poses of rigid objects from color images, of articulated objects from depth maps, or the camera location and orientation from images taken in urban environments. These methods have been applied to Robotics and Augmented Reality applications.
Contact: vincent.lepetit@u-bordeaux.fr
3D Object Pose Estimation from Color Images

3D Hand Pose Estimation from Depth Maps
Related Publications
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images. Mahdi Rad, Markus Oberweger, and Vincent Lepetit. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth. Mahdi Rad and Vincent Lepetit. In Proceedings of the International Conference on Computer Vision (ICCV), 2017.
DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation. In Workshop at the International Conference on Computer Vision (ICCV Workshop), 2017.
Learning Lightprobes for Mixed Reality Illumination. In Proceedings of the International Symposium on Mixed and Augmented Reality (ISMAR), 2017.
ALCN: Adaptive Local Contrast Normalization for Robust Object Detection and 3D Pose Estimation. In Proceedings of the British Machine Vision Conference (BMVC), 2017.
Accurate Camera Registration in Urban Environments using High-Level Feature Matching. In Proceedings of the British Machine Vision Conference (BMVC), 2017.
Learning to Align Semantic Segmentation and 2.5D Maps for Geolocalization In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Robust 3D Object Tracking from Monocular Images using Stable PartsIEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2017.
Detecting Flying Objects using a Single Moving CameraIEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2017.
LIFT: Learned Invariant Feature Transform In Proceedings of the European Conference on Computer Vision (ECCV), 2016.
Structured Prediction of 3D Human Pose with Deep Neural Networks In Proceedings of the British Machine Vision Conference (BMVC), 2016.
Automated Age Estimation from Hand MRI Volumes using Deep Learning In Proceedings of the Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2016.
Learning Image Descriptors with BoostingIEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2015.
Training a Feedback Loop for Hand Pose Estimation In Proceedings of the International Conference on Computer Vision (ICCV), 2015.
Projection onto the Manifold of Elongated Structures for Accurate Extraction In Proceedings of the International Conference on Computer Vision (ICCV), 2015.
On Rendering Synthetic Images for Training an Object DetectorComputer Vision and Image Understanding (CVIU), 2015.
Learning Separable FiltersIEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2015
You Should Use Regression to Detect Cells In Proceedings of the Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015.
Learning Descriptors for Object Recognition and 3D Pose Estimation In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.