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Model-free Consensus Maximization for Non-Rigid Shapes
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018Many computer vision methods use consensus maximization to relate measurements containing outliers with the correct transformation model. In the context of rigid shapes, this is typically done using Random Sampling and Consensus (RANSAC) by estimating an analytical model that agrees with the largest number of measurements (inliers). However, small parameter models may not be always available. In this paper, we formulate the model-free ...Conference Paper -
Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIIIThe perspective camera and the isometric surface prior have recently gathered increased attention for Non-Rigid Structure-from-Motion (NRSfM). Despite the recent progress, several challenges remain, particularly the computational complexity and the unknown camera focal length. In this paper we present a method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the perspective camera model and the isometric surface prior with ...Conference Paper -
Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation
(2017)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data. We propose to model the statistical relationships of 3D hand poses and corresponding depth images using two deep generative models with a shared latent space. By design, our architecture allows for learning from unlabeled image data in a semi-supervised manner. Assuming a one-to-one mapping between a pose and a depth ...Conference Paper -
Deeply Learned 2D Tool Pose Estimation for Robot-to-Camera Registration
(2017)Proceedings of the 7th Joint Workshop on New Technologies for Computer/Robot Assisted SurgeryConference Paper -
Combining human body shape and pose estimation for robust upper body tracking using a depth sensor
(2016)Lecture Notes in Computer Science ~ Computer Vision - ECCV 2016 WorkshopsConference Paper -
Self-Supervised 3D Hand Pose Estimation Through Training by Fitting
(2019)We present a self-supervision method for 3D hand pose estimation from depth maps. We begin with a neural network initialized with synthesized data and fine-tune it on real but unlabelled depth maps by minimizing a set of datafitting terms. By approximating the hand surface with a set of spheres, we design a differentiable hand renderer to align estimates by comparing the rendered and input depth maps. In addition, we place a set of priors ...Conference Paper -
Self-supervised 3D hand pose estimation through training by fitting
(2019)Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)Conference Paper -
Unsupervised Learning of Consensus Maximization for 3D Vision Problems
(2019)Consensus maximization is a key strategy in 3D vision for robust geometric model estimation from measurements with outliers. Generic methods for consensus maximization, such as Random Sampling and Consensus (RANSAC), have played a tremendous role in the success of 3D vision, in spite of the ubiquity of outliers. However, replicating the same generic behaviour in a deeply learned architecture, using supervised approaches, has proven to be ...Conference Paper -
What Correspondences Reveal About Unknown Camera and Motion Models?
(2019)In two-view geometry, camera models and motion types are used as key knowledge along with the image point correspondences in order to solve several key problems of 3D vision. Problems such as Structure-from-Motion (SfM) and camera self-calibration are tackled under the assumptions of a specific camera projection model and motion type. However, these key assumptions may not be always justified, i.e., we may often know neither the camera ...Conference Paper -
Mapping, localization and path planning for image-based navigation using visual features and map
(2019)Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)Conference Paper