Search Results for author: Friedrich Fraundorfer

Found 26 papers, 6 papers with code

End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB

no code implementations12 Jul 2021 Stefan Ainetter, Friedrich Fraundorfer

In this work, we introduce a novel, end-to-end trainable CNN-based architecture to deliver high quality results for grasp detection suitable for a parallel-plate gripper, and semantic segmentation.

Semantic Segmentation

MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans

no code implementations20 Mar 2021 Sinisa Stekovic, Mahdi Rad, Friedrich Fraundorfer, Vincent Lepetit

For this step, we propose a novel differentiable method for rendering the polygonal shapes of these proposals.

BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo

no code implementations23 Oct 2020 Christian Sormann, Patrick Knöbelreiter, Andreas Kuhn, Mattia Rossi, Thomas Pock, Friedrich Fraundorfer

We therefore show how we can calculate a normalization based on the expected 3D error, which we can then use to normalize the label jumps in the CRF.

FC-DCNN: A densely connected neural network for stereo estimation

2 code implementations14 Oct 2020 Dominik Hirner, Friedrich Fraundorfer

The output of this network is used in order to calculate matching costs and create a cost-volume.

Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images

no code implementations24 Jul 2020 Stefano Zorzi, Ksenia Bittner, Friedrich Fraundorfer

In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps.

Machine-learned Regularization and Polygonization of Building Segmentation Masks

no code implementations24 Jul 2020 Stefano Zorzi, Ksenia Bittner, Friedrich Fraundorfer

We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks.

Regularization of Building Boundaries in Satellite Images using Adversarial and Regularized Losses

no code implementations23 Jul 2020 Stefano Zorzi, Friedrich Fraundorfer

In this paper we present a method for building boundary refinement and regularization in satellite images using a fully convolutional neural network trained with a combination of adversarial and regularized losses.

Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences

no code implementations21 Jul 2020 Banglei Guan, Ji Zhao, Daniel Barath, Friedrich Fraundorfer

We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs).

Motion Estimation Pose Estimation

Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems

1 code implementation13 Mar 2020 Patrick Knöbelreiter, Christian Sormann, Alexander Shekhovtsov, Friedrich Fraundorfer, Thomas Pock

It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models.

Optical Flow Estimation Semantic Segmentation

General 3D Room Layout from a Single View by Render-and-Compare

1 code implementation ECCV 2020 Sinisa Stekovic, Shreyas Hampali, Mahdi Rad, Sayan Deb Sarkar, Friedrich Fraundorfer, Vincent Lepetit

In order to deal with occlusions between components of the layout, which is a problem ignored by previous works, we introduce an analysis-by-synthesis method to iteratively refine the 3D layout estimate.

Minimal Solutions for Relative Pose with a Single Affine Correspondence

no code implementations CVPR 2020 Banglei Guan, Ji Zhao, Zhang Li, Fang Sun, Friedrich Fraundorfer

In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases.

Affine Transformation Motion Estimation +2

Casting Geometric Constraints in Semantic Segmentation as Semi-Supervised Learning

no code implementations29 Apr 2019 Sinisa Stekovic, Friedrich Fraundorfer, Vincent Lepetit

We propose a simple yet effective method to learn to segment new indoor scenes from video frames: State-of-the-art methods trained on one dataset, even as large as the SUNRGB-D dataset, can perform poorly when applied to images that are not part of the dataset, because of the dataset bias, a common phenomenon in computer vision.

Semantic Segmentation

S4-Net: Geometry-Consistent Semi-Supervised Semantic Segmentation

no code implementations27 Dec 2018 Sinisa Stekovic, Friedrich Fraundorfer, Vincent Lepetit

We show that it is possible to learn semantic segmentation from very limited amounts of manual annotations, by enforcing geometric 3D constraints between multiple views.

Semi-Supervised Semantic Segmentation

Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization

no code implementations ECCV 2018 Thomas Holzmann, Michael Maurer, Friedrich Fraundorfer, Horst Bischof

We propose a method for urban 3D reconstruction, which incorporates semantic information and plane priors within the reconstruction process in order to generate visually appealing 3D models.

3D Reconstruction

Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks

no code implementations14 Aug 2018 Muhammad Shahzad, Michael Maurer, Friedrich Fraundorfer, Yuanyuan Wang, Xiao Xiang Zhu

This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high resolution (VHR) synthetic aperture radar (SAR) images.

General Classification Image Classification

Deep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems

no code implementations9 May 2018 Georg Waltner, Michael Maurer, Thomas Holzmann, Patrick Ruprecht, Michael Opitz, Horst Possegger, Friedrich Fraundorfer, Horst Bischof

Furthermore due to the design of the network, at test time only the 2D camera images are required for classification which enables the usage in portable computer vision systems.

3D Object Classification Classification +2

Evaluation of CNN-based Single-Image Depth Estimation Methods

no code implementations3 May 2018 Tobias Koch, Lukas Liebel, Friedrich Fraundorfer, Marco Körner

While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited.

Depth Estimation

Prioritized Multi-View Stereo Depth Map Generation Using Confidence Prediction

no code implementations22 Mar 2018 Christian Mostegel, Friedrich Fraundorfer, Horst Bischof

In the second step, we rank the resulting view clusters (i. e. key views with matching partners) according to their impact on the fulfillment of desired quality parameters such as completeness, ground resolution and accuracy.

3D Reconstruction

Deep learning in remote sensing: a review

1 code implementation11 Oct 2017 Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer

In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with.

Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity

no code implementations CVPR 2017 Christian Mostegel, Rudolf Prettenthaler, Friedrich Fraundorfer, Horst Bischof

In this paper we present a scalable approach for robustly computing a 3D surface mesh from multi-scale multi-view stereo point clouds that can handle extreme jumps of point density (in our experiments three orders of magnitude).

UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality Assurance by Confidence Prediction

no code implementations6 May 2016 Christian Mostegel, Markus Rumpler, Friedrich Fraundorfer, Horst Bischof

In this paper we present an autonomous system for acquiring close-range high-resolution images that maximize the quality of a later-on 3D reconstruction with respect to coverage, ground resolution and 3D uncertainty.

3D Reconstruction

Using Self-Contradiction to Learn Confidence Measures in Stereo Vision

no code implementations CVPR 2016 Christian Mostegel, Markus Rumpler, Friedrich Fraundorfer, Horst Bischof

Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision.

Relative Pose Estimation for a Multi-Camera System with Known Vertical Direction

no code implementations CVPR 2014 Gim Hee Lee, Marc Pollefeys, Friedrich Fraundorfer

In this paper, we present our minimal 4-point and linear 8-point algorithms to estimate the relative pose of a multi-camera system with known vertical directions, i. e. known absolute roll and pitch angles.

Pose Estimation

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