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A Streamlined Attention-Based Network for Descriptor Extraction
Mattia D'Urso, Emanuele Santellani, Christian Sormann +3
We introduce SANDesc, a Streamlined Attention-Based Network for Descriptor extraction that aims to improve on existing architectures for keypoint description. Our descriptor networ…
GMM-IKRS: Gaussian Mixture Models for Interpretable Keypoint Refinement and Scoring
Emanuele Santellani, Martin Zach, Christian Sormann +3
The extraction of keypoints in images is at the basis of many computer vision applications, from localization to 3D reconstruction. Keypoints come with a score permitting to rank t…
S-TREK: Sequential Translation and Rotation Equivariant Keypoints for local feature extraction
Emanuele Santellani, Christian Sormann, Mattia Rossi +2
In this work we introduce S-TREK, a novel local feature extractor that combines a deep keypoint detector, which is both translation and rotation equivariant by design, with a light…
DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo
Christian Sormann, Emanuele Santellani, Mattia Rossi +2
We propose a novel approach for deep learning-based Multi-View Stereo (MVS). For each pixel in the reference image, our method leverages a deep architecture to search for the corre…
MD-Net: Multi-Detector for Local Feature Extraction
Emanuele Santellani, Christian Sormann, Mattia Rossi +2
Establishing a sparse set of keypoint correspon dences between images is a fundamental task in many computer vision pipelines. Often, this translates into a computationally expensi…