no code implementations • 30 Apr 2024 • Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. Nascimento
Besides, our model is designed to offer the choice of matching at the sparse or semi-dense levels, each of which may be more suitable for different downstream applications, such as visual navigation and augmented reality.
1 code implementation • 22 Dec 2023 • Joaquin Rodriguez, Lew-Fock-Chong Lew-Yan-Voon, Renato Martins, Olivier Morel
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.
1 code implementation • 2 Nov 2023 • Hermes McGriff, Renato Martins, Nicolas Andreff, Cédric Demonceaux
In this paper, we propose an approach to address the problem of 3D reconstruction of scenes from a single image captured by a light-field camera equipped with a rolling shutter sensor.
1 code implementation • 1 Sep 2023 • Felipe Cadar, Welerson Melo, Vaishnavi Kanagasabapathi, Guilherme Potje, Renato Martins, Erickson R. Nascimento
We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence.
1 code implementation • CVPR 2023 • Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. Nascimento
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval.
1 code implementation • 13 Dec 2022 • Welerson Melo, Guilherme Potje, Felipe Cadar, Renato Martins, Erickson R. Nascimento
We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence.
1 code implementation • 29 Aug 2022 • Joaquin Rodriguez, Lew Lew-Yan-Voon, Renato Martins, Olivier Morel
In this paper, we propose a new method to overcome the need for complex optical systems to efficiently calibrate these cameras.
no code implementations • 22 Mar 2022 • Guilherme Potje, Renato Martins, Felipe Cadar, Erickson R. Nascimento
Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfaces.
1 code implementation • NeurIPS 2021 • Guilherme Potje, Renato Martins, Felipe Cadar, Erickson R. Nascimento
Despite the advances in extracting local features achieved by handcrafted and learning-based descriptors, they are still limited by the lack of invariance to non-rigid transformations.
no code implementations • 22 Oct 2021 • Thiago L. Gomes, Thiago M. Coutinho, Rafael Azevedo, Renato Martins, Erickson R. Nascimento
It also infers texture appearance with a convolutional network in the texture domain, which is trained in an adversarial regime to reconstruct human texture from rendered images of actors in different poses.
no code implementations • 29 Mar 2021 • Thiago L. Gomes, Renato Martins, João Ferreira, Rafael Azevedo, Guilherme Torres, Erickson R. Nascimento
Transferring human motion and appearance between videos of human actors remains one of the key challenges in Computer Vision.
1 code implementation • 25 Nov 2020 • João P. Ferreira, Thiago M. Coutinho, Thiago L. Gomes, José F. Neto, Rafael Azevedo, Renato Martins, Erickson R. Nascimento
Our method uses an adversarial learning scheme conditioned on the input music audios to create natural motions preserving the key movements of different music styles.
1 code implementation • 13 Mar 2020 • Renato Martins, Dhiego Bersan, Mario F. M. Campos, Erickson R. Nascimento
The formulation is designed to identify and to disregard dynamic objects in order to obtain a medium-term invariant map representation.
no code implementations • 8 Jan 2020 • Thiago L. Gomes, Renato Martins, João Ferreira, Erickson R. Nascimento
Differently from recent appearance transferring methods, our approach takes into account body shape, appearance, and motion constraints.