22 citations
- ImViA - Imagerie et Vision ArtificielleFR7 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Université de BourgogneFR5 papers
- Instituto de Investigación en Ingeniería de AragónES2 papers
- Universidade Federal de Minas GeraisBR2 papers
- Université Bourgogne Franche-ComtéFR2 papers
- Alstom (France)FR1 paper
- Infection et inflammationFR1 paper
- Laboratoire d'Informatique, Signaux et Systèmes de Sophia AntipolisFR1 paper
- Laboratoire Interdisciplinaire Carnot de BourgogneFR1 paper
- Universidad de ZaragozaES1 paper
- Vanda InstituteKH1 paper
8 papers
Leveraging Foundation Models To learn the shape of semi-fluid deformable objects
Omar El Assal, Carlos M. Mateo, Sebastien Ciron +1
One of the difficulties imposed on the manipulation of deformable objects is their characterization and the detection of representative keypoints for the purpose of manipulation. A…
Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarization
Joaquin Rodriguez, Lew-Fock-Chong Lew-Yan-Voon, Renato Martins +1
Polarization information of the light can provide rich cues for computer vision and scene understanding tasks, such as the type of material, pose, and shape of the objects. With th…
Learning Geodesic-Aware Local Features from RGB-D Images
Guilherme Potje, Renato Martins, Felipe Cadar +1
Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfa…
Modality-Guided Subnetwork for Salient Object Detection
Zongwei Wu, Guillaume Allibert, Christophe Stolz +2
Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the…
Creating and Reenacting Controllable 3D Humans with Differentiable Rendering
Thiago L. Gomes, Thiago M. Coutinho, Rafael Azevedo +2
This paper proposes a new end-to-end neural rendering architecture to transfer appearance and reenact human actors. Our method leverages a carefully designed graph convolutional ne…
Depth-Adapted CNN for RGB-D cameras
Zongwei Wu, Guillaume Allibert, Christophe Stolz +1
Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the ph…