1 citations · 1 across the 9 of their papers we have counts for
11 papers
EPO: Boosting 3D Foundation Models with Edge-based Pose Optimization
Mattia D'Urso, Christian Sormann, Mattia Rossi +1
We introduce \textbf{Edge-based Pose Optimization (EPO)}, a trackless geometric optimization framework specifically designed to boost the Structure-from-Motion reconstructions gene…
TerraSky3D: Multi-View Reconstructions of European Landmarks in 4K
Mattia D'Urso, Yuxi Hu, Christian Sormann +2
Despite the growing need for data of more and more sophisticated 3D reconstruction pipelines, we can still observe a scarcity of suitable public datasets. Existing 3D datasets are…
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…
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
Patrick Knöbelreiter, Christian Sormann, Alexander Shekhovtsov +2
It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main diffi…
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…