90 citations · 152 across the 13 of their papers we have counts for
12 papers · 1 filter
Leveraging Semantic Cues from Foundation Vision Models for Enhanced Local Feature Correspondence
Felipe Cadar, Guilherme Potje, Renato Martins +2
Visual correspondence is a crucial step in key computer vision tasks, including camera localization, image registration, and structure from motion. The most effective techniques fo…
XFeat: Accelerated Features for Lightweight Image Matching
Guilherme Potje, Felipe Cadar, Andre Araujo +2
We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method, dubbed XFeat (Accelerated Features), revisits fundamental design choi…
Improving the matching of deformable objects by learning to detect keypoints
Felipe Cadar, Welerson Melo, Vaishnavi Kanagasabapathi +3
We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence. By leveraging true correspondences ac…
Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints
Guilherme Potje, Felipe Cadar, Andre Araujo +2
Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that im…
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images
Welerson Melo, Guilherme Potje, Felipe Cadar +2
We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence. Our training framework uses…
A Practical Calibration Method for RGB Micro-Grid Polarimetric Cameras
Joaquin Rodriguez, Lew Lew-Yan-Voon, Renato Martins +1
Polarimetric imaging has been applied in a growing number of applications in robotic vision (ex. underwater navigation, glare removal, de-hazing, object classification, and depth e…
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…
Extracting Deformation-Aware Local Features by Learning to Deform
Guilherme Potje, Renato Martins, Felipe Cadar +1
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 transformati…
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…
A Shape-Aware Retargeting Approach to Transfer Human Motion and Appearance in Monocular Videos
Thiago L. Gomes, Renato Martins, João Ferreira +3
Transferring human motion and appearance between videos of human actors remains one of the key challenges in Computer Vision. Despite the advances from recent image-to-image transl…
Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues
Renato Martins, Dhiego Bersan, Mario F. M. Campos +1
This paper addresses the problem of building augmented metric representations of scenes with semantic information from RGB-D images. We propose a complete framework to create an en…
Do As I Do: Transferring Human Motion and Appearance between Monocular Videos with Spatial and Temporal Constraints
Thiago L. Gomes, Renato Martins, João Ferreira +1
Creating plausible virtual actors from images of real actors remains one of the key challenges in computer vision and computer graphics. Marker-less human motion estimation and sha…