Renato Martins

I am an Assistant Professor (Maître de Conférences) at Université de Bourgogne (UB), working in the "Laboratoire Interdisciplinaire Carnot de Bourgogne" (ICB, CO2M team), a joint research unity between the CNRS and UB in Dijon. My teaching activies are in the Department of Robotics (Le Creusot) of the engineering school of UB (ESIREM). I am also an Associate Member of the TANGRAM team at INRIA Nancy/LORIA.

Previously, I was a member of the "Laboratoire d'Imagerie et Vision Artificielle" (ImViA) until May 2023. I did my PhD at INRIA Sophia Antipolis/France and at Ecole des Mines de Paris / Université Paris Sciences et Lettres, where I was advised by Dr. Patrick Rives. Then, I was a post-doctoral researcher in the CNRS I3S laboratory, in the ACENTAURI / CHORALE group at INRIA and with the VeRLab laboratory at Universidade Federal de Minas Gerais/Brazil, with whom I maintain tight research collaborations.

My research interests lie in Computer Vision, Machine Learning and Robot Vision, more specifically in the topics of 3D vision, geometric deep learning, human motion analysis, video prediction, and RGB-D image analysis and processing (perspective, omnidirectional).

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Community Service & Reviewing Activities
Recent Papers

Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints
Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, and Erickson R. Nascimento
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
arXiv / project webpage / bibtex / github code

This paper proposes a learning-based keypoint detection and description approach to describe images of rigid and non-rigid objects. Both detection and description of keypoints are learned jointly and they are done simultaneously at test time.

Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images
Welerson Melo, Guilherme Potje, Felipe Cadar, Renato Martins, and Erickson R. Nascimento
Conference on Graphics, Patterns and Images (SIBGRAPI), 2022
arXiv / project webpage / bibtex / github code

In this paper, we present a novel detection method for locating confident keypoints on images affected by non-rigid deformations. Our detector learns to locate keypoints given supervision from visual correspondences, obtained by matching annotated image pairs with a predefined descriptor extractor.

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