135 citations · 136 across the 9 of their papers we have counts for
10 papers · 1 filter
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…
IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions
Christian Sormann, Mattia Rossi, Andreas Kuhn +1
We present a novel deep-learning-based method for Multi-View Stereo. Our method estimates high resolution and highly precise depth maps iteratively, by traversing the continuous sp…
BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo
Christian Sormann, Patrick Knöbelreiter, Andreas Kuhn +3
In this work, we propose BP-MVSNet, a convolutional neural network (CNN)-based Multi-View-Stereo (MVS) method that uses a differentiable Conditional Random Field (CRF) layer for re…
Joint Graph-based Depth Refinement and Normal Estimation
Mattia Rossi, Mireille El Gheche, Andreas Kuhn +1
Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a…
DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction
Andreas Kuhn, Christian Sormann, Mattia Rossi +2
Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D reconstructions. However, the use of DNNs in the context of 3D reconstruction from large and…
Light Field Super-Resolution Via Graph-Based Regularization
Mattia Rossi, Pascal Frossard
Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from…