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Dive into the research topics where Fabian Langguth is active.

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Featured researches published by Fabian Langguth.


computer vision and pattern recognition | 2012

Photometric stereo for outdoor webcams

Jens Ackermann; Fabian Langguth; Simon Fuhrmann; Michael Goesele

We present a photometric stereo technique that operates on time-lapse sequences captured by static outdoor webcams over the course of several months. Outdoor webcams produce a large set of uncontrolled images subject to varying lighting and weather conditions. We first automatically select a suitable subset of the captured frames for further processing, reducing the dataset size by several orders of magnitude. A camera calibration step is applied to recover the camera response function, the absolute camera orientation, and to compute the light directions for each image. Finally, we describe a new photometric stereo technique for non-Lambertian scenes and unknown light source intensities to recover normal maps and spatially varying materials of the scene.


eurographics | 2014

MVE: a multi-view reconstruction environment

Simon Fuhrmann; Fabian Langguth; Michael Goesele

We present MVE, the Multi-View Environment. MVE is an end-to-end multi-view geometry reconstruction software which takes photos of a scene as input and produces a surface triangle mesh as result. The system covers a structure-from-motion algorithm, multi-view stereo reconstruction, generation of extremely dense point clouds, and reconstruction of surfaces from point clouds. In contrast to most image-based geometry reconstruction approaches, our system is focused on reconstruction of multi-scale scenes, an important aspect in many areas such as cultural heritage. It allows to reconstruct large datasets containing some detailed regions with much higher resolution than the rest of the scene. Our system provides a graphical user interface for structure-from-motion reconstruction, visual inspection of images, depth maps, and rendering of scenes and meshes.


Computers & Graphics | 2015

MVE-An image-based reconstruction environment

Simon Fuhrmann; Fabian Langguth; Nils Moehrle; Michael Waechter; Michael Goesele

We present an image-based reconstruction system, the Multi-View Environment. MVE is an end-to-end multi-view geometry reconstruction software which takes photos of a scene as input and produces a textured surface mesh as result. The system covers a structure-from-motion algorithm, multi-view stereo reconstruction, generation of extremely dense point clouds, reconstruction of surfaces from point clouds, and surface texturing. In contrast to most image-based geometry reconstruction approaches, our system is focused on reconstruction of multi-scale scenes, an important aspect in many areas such as cultural heritage. It allows to reconstruct large datasets containing some detailed regions with much higher resolution than the rest of the scene. Our system provides a graphical user interface for visual inspection of the individual steps of the pipeline, i.e., the structure-from-motion result, multi-view stereo depth maps, and rendering of scenes and meshes. Display Omitted HighlightsEnd-to-end multi-view geometry reconstruction and texturing pipeline.Multi-scale reconstruction approach.


international conference on computer graphics and interactive techniques | 2013

Image-based rendering in the gradient domain

Johannes Kopf; Fabian Langguth; Daniel Scharstein; Richard Szeliski; Michael Goesele

We propose a novel image-based rendering algorithm for handling complex scenes that may include reflective surfaces. Our key contribution lies in treating the problem in the gradient domain. We use a standard technique to estimate scene depth, but assign depths to image gradients rather than pixels. A novel view is obtained by rendering the horizontal and vertical gradients, from which the final result is reconstructed through Poisson integration using an approximate solution as a data term. Our algorithm is able to handle general scenes including reflections and similar effects without explicitly separating the scene into reflective and transmissive parts, as required by previous work. Our prototype renderer is fully implemented on the GPU and runs in real time on commodity hardware.


IEEE Computer | 2010

Scene Reconstruction from Community Photo Collections

Michael Goesele; Jens Ackermann; Simon Fuhrmann; Ronny Klowsky; Fabian Langguth; Patrick Mücke; Martin Ritz

The literally billions of images available from online photo-sharing sites offer an I unprecedented wealth of information but also add additional layers of complexity for reconstruction applications.


european conference on computer vision | 2016

Shading-Aware Multi-view Stereo

Fabian Langguth; Kalyan Sunkavalli; Sunil Hadap; Michael Goesele

We present a novel multi-view reconstruction approach that effectively combines stereo and shape-from-shading energies into a single optimization scheme. Our method uses image gradients to transition between stereo-matching (which is more accurate at large gradients) and Lambertian shape-from-shading (which is more robust in flat regions). In addition, we show that our formulation is invariant to spatially varying albedo without explicitly modeling it. We show that the resulting energy function can be optimized efficiently using a smooth surface representation based on bicubic patches, and demonstrate that this algorithm outperforms both previous multi-view stereo algorithms and shading based refinement approaches on a number of datasets.


international conference on 3d vision | 2014

Multi-view Photometric Stereo by Example

Jens Ackermann; Fabian Langguth; Simon Fuhrmann; Arjan Kuijper; Michael Goesele

We present a novel multi-view photometric stereo technique that recovers the surface of texture less objects with unknown BRDF and lighting. The camera and light positions are allowed to vary freely and change in each image. We exploit orientation consistency between the target and an example object to develop a consistency measure. Motivated by the fact that normals can be recovered more reliably than depth, we represent our surface as both a depth map and a normal map. These maps are jointly optimized and allow us to formulate constraints on depth that take surface orientation into account. Our technique does not require the visual hull or stereo reconstructions for bootstrapping and solely exploits image intensities without the need for radiometric camera calibration. We present results on real objects with varying degree of specularity and show that these can be used to create globally consistent models from multiple views.


eurographics | 2013

Guided Capturing of Multi-view Stereo Datasets

Fabian Langguth; Michael Goesele

We present an application for mobile devices, that allows any user, even without background in computer vision, to capture a complete set of images, that is suitable for a multi-view stereo reconstruction. Compared to related tasks, such as panorama capture, this setting is much harder, as the camera needs to move unrestricted in 3D space. Our system uses structure from motion to register captured images and generates a sparse reconstruction of the scene. The dataset is built in an incremental procedure, where the next best view is computed with a novel view planning strategy, that aims for a good coverage of the scene. The user is then guided towards the new view, and the image is captured automatically at the right position. The next iteration starts after the reconstruction has been updated. The quality of the resulting dataset is on par with datasets captured by an expert user.


Archive | 2014

MVE-A Multiview Reconstruction Environment

Simon Fuhrmann; Fabian Langguth; Michael Goesele


Computers & Graphics | 2012

Cultural Heritage: High resolution acquisition of detailed surfaces with lens-shifted structured light

Martin Ritz; Fabian Langguth; Manuel Scholz; Michael Goesele; André Stork

Collaboration


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Michael Goesele

Technische Universität Darmstadt

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Simon Fuhrmann

Technische Universität Darmstadt

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Jens Ackermann

Technische Universität Darmstadt

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Patrick Seemann

Technische Universität Darmstadt

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Stefan Guthe

University of Tübingen

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André Stork

Technische Universität Darmstadt

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Manuel Scholz

Technische Universität Darmstadt

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Michael Waechter

Technische Universität Darmstadt

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Nils Moehrle

Technische Universität Darmstadt

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