Search Results for author: Silvano Galliani

Found 12 papers, 9 papers with code

DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion

1 code implementation CVPR 2021 Arda Düzçeker, Silvano Galliani, Christoph Vogel, Pablo Speciale, Mihai Dusmanu, Marc Pollefeys

We propose an online multi-view depth prediction approach on posed video streams, where the scene geometry information computed in the previous time steps is propagated to the current time step in an efficient and geometrically plausible way.

Depth Estimation Depth Prediction

HoloLens 2 Research Mode as a Tool for Computer Vision Research

1 code implementation25 Aug 2020 Dorin Ungureanu, Federica Bogo, Silvano Galliani, Pooja Sama, Xin Duan, Casey Meekhof, Jan Stühmer, Thomas J. Cashman, Bugra Tekin, Johannes L. Schönberger, Pawel Olszta, Marc Pollefeys

Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.

Mixed Reality

Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network

3 code implementations12 Mar 2018 Charis Lanaras, José Bioucas-Dias, Silvano Galliani, Emmanuel Baltsavias, Konrad Schindler

The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution.

Super-Resolution

Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in Convolutional Networks

1 code implementation31 Jan 2018 Timo Hackel, Mikhail Usvyatsov, Silvano Galliani, Jan D. Wegner, Konrad Schindler

While CNNs naturally lend themselves to densely sampled data, and sophisticated implementations are available, they lack the ability to efficiently process sparse data.

Learned Multi-Patch Similarity

1 code implementation ICCV 2017 Wilfried Hartmann, Silvano Galliani, Michal Havlena, Luc van Gool, Konrad Schindler

Estimating a depth map from multiple views of a scene is a fundamental task in computer vision.

Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection

1 code implementation5 Dec 2016 Dimitrios Marmanis, Konrad Schindler, Jan Dirk Wegner, Silvano Galliani, Mihai Datcu, Uwe Stilla

We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries.

Boundary Detection Edge Detection +3

Just Look at the Image: Viewpoint-Specific Surface Normal Prediction for Improved Multi-View Reconstruction

no code implementations CVPR 2016 Silvano Galliani, Konrad Schindler

By training from known points in the same image, the prediction is specifically tailored to the materials and lighting conditions of the particular scene, as well as to the precise camera viewpoint.

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