34 citations · 95 across the 13 of their papers we have counts for
6 papers · 2 filters
Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach
Xi Shen, Ilaria Pastrolin, Oumayma Bounou +4
Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy sampl…
Learning elementary structures for 3D shape generation and matching
Theo Deprelle, Thibault Groueix, Matthew Fisher +3
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape. We…
Unsupervised cycle-consistent deformation for shape matching
Thibault Groueix, Matthew Fisher, Vladimir G. Kim +2
We propose a self-supervised approach to deep surface deformation. Given a pair of shapes, our algorithm directly predicts a parametric transformation from one shape to the other r…
Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects
Yang Xiao, Xuchong Qiu, Pierre-Alain Langlois +2
Most deep pose estimation methods need to be trained for specific object instances or categories. In this work we propose a completely generic deep pose estimation approach, which…
Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning
Xi Shen, Alexei A. Efros, Mathieu Aubry
Our goal in this paper is to discover near duplicate patterns in large collections of artworks. This is harder than standard instance mining due to differences in the artistic medi…
Virtual Training for a Real Application: Accurate Object-Robot Relative Localization without Calibration
Vianney Loing, Renaud Marlet, Mathieu Aubry
Localizing an object accurately with respect to a robot is a key step for autonomous robotic manipulation. In this work, we propose to tackle this task knowing only 3D models of th…