Aktuelle Publikationen

März 2019

Interactive and Multimodal-based Augmented Reality for Remote Assistance using a Digital Surgical Microscope

Eric Wisotzky, Peter Eisert, Anna Hilsmann, Jean-Claude Rosenthal, Florian Uecker, Armin Schneider, Falko Schmid, Michael Bauer

This paper introduces the current running project MultiARC, which has the aim to combine stereoscopic measurements and scene reconstruction with hyperspectral tissue differentiation in a surgical microscope and 3D endoscope for image-guided surgery.

Oktober 2018

Animatable 3D Model Generation from 2D Monocular Visual Data

Philipp Fechteler, Peter Eisert, Anna Hilsmann, Lisa Kausch

In this paper, we present an approach for creating animatable 3D models from temporal monocular image acquisitions of non-rigid objects. During deformation, the object of interest is captured with only a single camera under full perspective projection. The aim of the presented framework is to obtain a shape deformation model in terms of joints and skinning weights that can finally be used for animating the model vertices.

Oktober 2018

Markerless Closed-Loop Projection Plane Tracking for Mobile Projector-Camera Systems

Niklas Gard, Peter Eisert

The recent trend towards miniaturization of mobile projectors is allowing new forms of information presentation and interaction. Projectors can easily be moved freely in space either by humans or by mobile robots. This paper presents a technique to dynamically track the orientation and position of the projection plane only by analyzing the distortion of the projection by itself, independent of the presented content. It allows distortion-free projection with a fixed metric size for moving projector-camera systems.

März 2018

Automatic Analysis of Sewer Pipes Based on Unrolled Monocular Fisheye Images

Johannes Wolf Künzel, Peter Eisert, Thomas Werner, Ronja Möller, Jan Waschnewski, Ralf Hilpert

The task of detecting and classifying damages in sewer pipes offers an important application area for computer vision algorithms. This paper describes a system, which is capable of accomplishing this task solely based on low quality and severely compressed fisheye images from a pipe inspection robot. Relying on robust image features, we estimate camera poses, model the image lighting, and exploit this information to generate high quality cylindrical unwraps of the pipes' surfaces. Based on the generated images, we apply semantic labeling based on deep convolutional neural networks to detect and classify defects as well as structural elements.

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