Christian Reinbacher

Graz University of Technology

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Horst Bischof

Graz University of Technology

Thomas Pock

Graz University of Technology

Matthias Rüther

Joanneum Research

Georg Poier

Graz University of Technology

Matthias Zeppelzauer

St. Pölten University of Applied Sciences

Markus Seidl

St. Pölten University of Applied Sciences

Gottfried Graber

Institute of Computer Vision and Applied Computer Sciences

Alexander Shekhovtsov

Czech Technical University in Prague

Gottfried Munda

Graz University of Technology

Martin Schaich

Transnational Press London

Cooperative Institutions

Graz University of Technology

Institute of Computer Vision and Applied Computer Sciences

TU Wien

University of Vienna

BioTechMed-Graz

Joanneum Research

Karlsruhe Institute of Technology

Intel (United States)

Intel (Germany)

St. Pölten University of Applied Sciences

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PetroSurf3D - A Dataset for high-resolution 3D Surface Segmentation

arXiv (Cornell University) (2016)

Georg Poier Markus Seidl Matthias Zeppelzauer Christian Reinbacher Martin Schaich

The development of powerful 3D scanning hardware and reconstruction algorithms has strongly promoted the generation of 3D surface reconstructions in different domains. An area of special interest for such 3D reconstructions is the cultural heritage domain, where surface reconstructions are generated to digitally preserve historical artifacts. While reconstruction quality nowadays is sufficient in many cases, the robust analysis (e.g. segmentation, matching, and classification) of reconstructed 3D data is still an open topic. In this paper, we target the automatic and interactive segmentation of high-resolution 3D surface reconstructions from the archaeological domain. To foster research in this field, we introduce a fully annotated and publicly available large-scale 3D surface dataset including high-resolution meshes, depth maps and point clouds as a novel benchmark dataset to the community. We provide baseline results for our existing random forest-based approach and for the first time investigate segmentation with convolutional neural networks (CNNs) on the data. Results show that both approaches have complementary strengths and weaknesses and that the provided dataset represents a challenge for future research.

Benchmark (surveying)

3D Reconstruction

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Citations (6)

End-to-End Training of Hybrid CNN-CRF Models for Stereo

arXiv (Cornell University) (2016)

Patrick Knöbelreiter Christian Reinbacher Alexander Shekhovtsov Thomas Pock

We propose a novel and principled hybrid CNN+CRF model for stereo estimation. Our model allows to exploit the advantages of both, convolutional neural networks (CNNs) and conditional random fields (CRFs) in an unified approach. The CNNs compute expressive features for matching and distinctive color edges, which in turn are used to compute the unary and binary costs of the CRF. For inference, we apply a recently proposed highly parallel dual block descent algorithm which only needs a small fixed number of iterations to compute a high-quality approximate minimizer. As the main contribution of the paper, we propose a theoretically sound method based on the structured output support vector machine (SSVM) to train the hybrid CNN+CRF model on large-scale data end-to-end. Our trained models perform very well despite the fact that we are using shallow CNNs and do not apply any kind of post-processing to the final output of the CRF. We evaluate our combined models on challenging stereo benchmarks such as Middlebury 2014 and Kitti 2015 and also investigate the performance of each individual component.

CRFS

Unary operation

End-to-end principle

10.48550/arxiv.1611.10229

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Citations (1)

RoNect: Hand Mounted Depth Sensing Using a Commodity Depth Camera

Christian Reinbacher Matthias Rüther Horst Bischof

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Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization

arXiv (Cornell University) (2016)

Alexander Shekhovtsov Christian Reinbacher Gottfried Graber Thomas Pock

Dense image matching is a fundamental low-level problem in Computer Vision, which has received tremendous attention from both discrete and continuous optimization communities. The goal of this paper is to combine the advantages of discrete and continuous optimization in a coherent framework. We devise a model based on energy minimization, to be optimized by both discrete and continuous algorithms in a consistent way. In the discrete setting, we propose a novel optimization algorithm that can be massively parallelized. In the continuous setting we tackle the problem of non-convex regularizers by a formulation based on differences of convex functions. The resulting hybrid discrete-continuous algorithm can be efficiently accelerated by modern GPUs and we demonstrate its real-time performance for the applications of dense stereo matching and optical flow.

Discrete optimization

Minification

Optical Flow

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Citations (6)

Real-Time Panoramic Tracking for Event Cameras

arXiv (Cornell University) (2017)

Christian Reinbacher Gottfried Munda Thomas Pock

Event cameras are a paradigm shift in camera technology. Instead of full frames, the sensor captures a sparse set of events caused by intensity changes. Since only the changes are transferred, those cameras are able to capture quick movements of objects in the scene or of the camera itself. In this work we propose a novel method to perform camera tracking of event cameras in a panoramic setting with three degrees of freedom. We propose a direct camera tracking formulation, similar to state-of-the-art in visual odometry. We show that the minimal information needed for simultaneous tracking and mapping is the spatial position of events, without using the appearance of the imaged scene point. We verify the robustness to fast camera movements and dynamic objects in the scene on a recently proposed dataset and self-recorded sequences.

Robustness

Tracking (education)

Visual Odometry

Single camera

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Vision-Based Quality Inspection in Robotic Welding

Markus Heber Christian Reinbacher Matthias Rüther Horst Bischof

Robot welding

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Citations (2)

Fast variational multi-view segmentation through backprojection of spatial constraints

Image and Vision Computing (2012)

Christian Reinbacher Matthias Rüther Horst Bischof

10.1016/j.imavis.2012.08.009

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Citations (9)

RoμNect: Hand mounted depth sensing using a commodity gaming sensor

Deleted Journal (2012)

Christian Reinbacher Matthias Rüther Horst Bischof

In this work, we investigate the applicability of the Kinect depth camera as a robot mounted measurement unit. In contrast to traditional head mounted robot sensors, Kinect is small, cheap and delivers robust depth measurements on a variety of scenes. In the course of applying it on a robot arm, we solve a number of problems: we reduce the sensor working distance to a few centimeters, replace the Laser projector unit by a focusable projector, and calibrate this sensor unit. We further exploit the motion capabilities of the robot arm to integrate multiple depth maps with 30 Hz in a volumetric fusion approach. We show how this method considerably improves completeness of the scanned models, even under severe reflections and difficult surface properties. We employ our approach in a classical bin-picking setting, where the robot scans the object during its approaching motion, and picks it afterwards.

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Citations (3)

Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization

arXiv (Cornell University) (2016)

Alexander Shekhovtsov Christian Reinbacher Gottfried Graber Thomas Pock

Discrete optimization

Minification

Continuous modelling

10.48550/arxiv.1601.06274

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Citations (16)

Semi Automatic Segmentation of Articular Cartilage using Variational Methods

Christian Reinbacher

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