6 citations · 12 across the 9 of their papers we have counts for
13 papers
Rolling Shutter Camera Self-Calibration
Yongcong Zhang, Navid Rabbani, Bangyan Liao +3
Rolling shutter (RS) cameras are widely used in consumer devices, but their row-wise exposure causes distortions under motion, making geometric 3D vision problems dependent on both…
Freehand 2D Ultrasound Probe Calibration for Image Fusion with 3D MRI/CT
Yogesh Langhe, Katrin Skerl, Adrien Bartoli
The aim of this work is to implement a simple freehand ultrasound (US) probe calibration technique. This will enable us to visualize US image data during surgical procedures using…
Convex Relaxations for Isometric and Equiareal NRSfM
Agniva Sengupta, Adrien Bartoli
Extensible objects form a challenging case for NRSfM, owing to the lack of a sufficiently constrained extensible model of the point-cloud. We tackle the challenge by proposing 1) c…
Normal reconstruction from specularity in the endoscopic setting
Karim Makki, Adrien Bartoli
We show that for a plane imaged by an endoscope the specular isophotes are concentric circles on the scene plane, which appear as nested ellipses in the image. We show that these e…
The Proxy Step-size Technique for Regularized Optimization on the Sphere Manifold
Fang Bai, Adrien Bartoli
We give an effective solution to the regularized optimization problem , where is constrained on the unit sphere $\Vert \bo…
An Optimal Triangle Projector with Prescribed Area and Orientation, Application to Position-Based Dynamics
Carlos Arango Duque, Adrien Bartoli
The vast majority of mesh-based modelling applications iteratively transform the mesh vertices under prescribed geometric conditions. This occurs in particular in methods cycling t…
Robust Isometric Non-Rigid Structure-from-Motion
Shaifali Parashar, Adrien Bartoli, Daniel Pizarro
Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from the correspondences established between monocular 2D images. Current NRSfM methods lack statistical…
DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences
Jose Lamarca, Shaifali Parashar, Adrien Bartoli +1
Monocular SLAM algorithms perform robustly when observing rigid scenes, however, they fail when the observed scene deforms, for example, in medical endoscopy applications. We prese…
Assessing Capsule Networks With Biased Data
Bruno Ferrarini, Shoaib Ehsan, Adrien Bartoli +2
Machine learning based methods achieves impressive results in object classification and detection. Utilizing representative data of the visual world during the training phase is cr…
Deep Multi-class Adversarial Specularity Removal
John Lin, Mohamed El Amine Seddik, Mohamed Tamaazousti +2
We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single imag…
Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image
David Fuentes-Jimenez, David Casillas-Perez, Daniel Pizarro +2
We present Deep Shape-from-Template (DeepSfT), a novel Deep Neural Network (DNN) method for solving real-time automatic registration and 3D reconstruction of a deformable object vi…
Model-based active learning to detect isometric deformable objects in the wild with deep architectures
Shrinivasan Sankar, Adrien Bartoli
In the recent past, algorithms based on Convolutional Neural Networks (CNNs) have achieved significant milestones in object recognition. With large examples of each object class, s…
Rank-constrained fundamental matrix estimation by polynomial global optimization versus the eight-point algorithm
Florian Bugarin, Adrien Bartoli, Didier Henrion +3
The fundamental matrix can be estimated from point matches. The current gold standard is to bootstrap the eight-point algorithm and two-view projective bundle adjustment. The eight…