3 citations · 4 across the 2 of their papers we have counts for
4 papers · 1 filter
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…
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…