Search Results for author: Sebastian Bullinger

Found 14 papers, 6 papers with code

A Photogrammetry-based Framework to Facilitate Image-based Modeling and Automatic Camera Tracking

2 code implementations2 Dec 2020 Sebastian Bullinger, Christoph Bodensteiner, Michael Arens

We propose a framework that extends Blender to exploit Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques for image-based modeling tasks such as sculpting or camera and motion tracking.

3D Surface Reconstruction From Multi-Date Satellite Images

1 code implementation4 Feb 2021 Sebastian Bullinger, Christoph Bodensteiner, Michael Arens

The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry.

Camera Calibration Point cloud reconstruction +2

Geo-Tiles for Semantic Segmentation of Earth Observation Imagery

1 code implementation1 Jun 2023 Sebastian Bullinger, Florian Fervers, Christoph Bodensteiner, Michael Arens

This allows us to perform a tile specific data augmentation during training and a substitution of pixel predictions with limited context information using data of overlapping tiles during inference.

Data Augmentation Segmentation +1

Revisiting Click-based Interactive Video Object Segmentation

1 code implementation3 Mar 2022 Stephane Vujasinovic, Sebastian Bullinger, Stefan Becker, Norbert Scherer-Negenborn, Michael Arens, Rainer Stiefelhagen

While current methods for interactive Video Object Segmentation (iVOS) rely on scribble-based interactions to generate precise object masks, we propose a Click-based interactive Video Object Segmentation (CiVOS) framework to simplify the required user workload as much as possible.

Interactive Video Object Segmentation Object +3

3D Trajectory Reconstruction of Dynamic Objects Using Planarity Constraints

no code implementations16 Nov 2017 Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen

We apply Structure from Motion techniques to object and background images to determine for each frame camera poses relative to object instances and background structures.

Object Optical Flow Estimation +1

Instance Flow Based Online Multiple Object Tracking

no code implementations3 Mar 2017 Sebastian Bullinger, Christoph Bodensteiner, Michael Arens

The evaluation shows that our tracking approach is able to track objects with high relative motions.

Multiple Object Tracking Object +2

3D Vehicle Trajectory Reconstruction in Monocular Video Data Using Environment Structure Constraints

no code implementations ECCV 2018 Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen

We apply Structure from Motion techniques to vehicle and background images to determine for each frame camera poses relative to vehicle instances and background structures.

Optical Flow Estimation Semantic Segmentation

Integration of the 3D Environment for UAV Onboard Visual Object Tracking

no code implementations6 Aug 2020 Stéphane Vujasinović, Stefan Becker, Timo Breuer, Sebastian Bullinger, Norbert Scherer-Negenborn, Michael Arens

The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking.

3D Reconstruction Object +2

Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors

no code implementations7 Mar 2022 Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen

Our method is the first to utilize on-board cameras in an end-to-end differentiable model for metric self-localization on unseen orthophotos.

Metric Learning

Uncertainty-aware Vision-based Metric Cross-view Geolocalization

no code implementations CVPR 2023 Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen

This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose.

Autonomous Driving Pseudo Label

C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation

no code implementations13 Dec 2023 Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens, Rainer Stiefelhagen

To find the geolocation of a street-view image, cross-view geolocalization (CVGL) methods typically perform image retrieval on a database of georeferenced aerial images and determine the location from the visually most similar match.

Image Retrieval Pose Estimation +1

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