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Silvano Galliani
Researcher at ETH Zurich
Publications - 24
Citations - 2817
Silvano Galliani is an academic researcher from ETH Zurich. The author has contributed to research in topics: Convolutional neural network & Filter (signal processing). The author has an hindex of 11, co-authored 24 publications receiving 1747 citations. Previous affiliations of Silvano Galliani include Saarland University & Microsoft.
Papers
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Proceedings ArticleDOI
A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos
Thomas Schops,Johannes L. Schonberger,Silvano Galliani,Torsten Sattler,Konrad Schindler,Marc Pollefeys,Andreas Geiger +6 more
TL;DR: This benchmark is the first to cover the important use case of hand-held mobile devices while also providing high-resolution DSLR camera images and provides data at significantly higher temporal and spatial resolution.
Journal ArticleDOI
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
Dimitrios Marmanis,Dimitrios Marmanis,Konrad Schindler,Jan Dirk Wegner,Silvano Galliani,Mihai Datcu,Uwe Stilla +6 more
TL;DR: This work constructs a comparatively simple, memory-efficient model by adding boundary detection to the Segnet encoder-decoder architecture, and includes boundary detection in FCN-type models and sets up a high-end classifier ensemble, showing that boundary detection significantly improves semantic segmentation with CNNs.
Proceedings ArticleDOI
Massively Parallel Multiview Stereopsis by Surface Normal Diffusion
TL;DR: This work builds on the Patchmatch idea: starting from randomly generated 3D planes in scene space, the best-fitting planes are iteratively propagated and refined to obtain a 3D depth and normal field per view, such that a robust photo-consistency measure over all images is maximized.
Posted Content
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
Dimitrios Marmanis,Dimitrios Marmanis,Konrad Schindler,Jan Dirk Wegner,Silvano Galliani,Mihai Datcu,Uwe Stilla +6 more
TL;DR: In this article, the authors propose to combine semantic segmentation with semantically informed edge detection, thus making class-boundaries explicit in the model, and achieve state-of-the-art performance on the ISPRS Vaihingen benchmark.
Journal ArticleDOI
Semantic segmentation of aerial images with an ensemble of cnns
Dimitrios Marmanis,Dimitrios Marmanis,Jan Dirk Wegner,Silvano Galliani,Konrad Schindler,Mihai Datcu,Uwe Stilla +6 more
TL;DR: A FCN is designed which takes as input intensity and range data and, with the help of aggressive deconvolution and recycling of early network layers, converts them into a pixelwise classification at full resolution, and discusses design choices and intricacies of such a network.