6 citations · 17 across the 7 of their papers we have counts for
7 papers
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…
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…