no code implementations • ECCV 2020 • Ahmed Samy Nassar, Stefano D’Aronco, Sébastien Lefèvre, Jan D. Wegner
In this paper, we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object.
no code implementations • 4 Sep 2023 • Yuchang Jiang, Marius Rüetschi, Vivien Sainte Fare Garnot, Mauro Marty, Konrad Schindler, Christian Ginzler, Jan D. Wegner
Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.
1 code implementation • CVPR 2023 • Nikolai Kalischek, Rodrigo Caye Daudt, Torben Peters, Reinhard Furrer, Jan D. Wegner, Konrad Schindler
With the release of BiasBed, we hope to foster a common understanding of consistent and meaningful comparisons, and consequently faster progress towards learning methods free of texture bias.
1 code implementation • 23 Nov 2022 • Nikolai Kalischek, Rodrigo C. Daudt, Torben Peters, Reinhard Furrer, Jan D. Wegner, Konrad Schindler
With the release of BiasBed, we hope to foster a common understanding of consistent and meaningful comparisons, and consequently faster progress towards learning methods free of texture bias.
1 code implementation • 23 Nov 2022 • Nikolai Kalischek, Torben Peters, Jan D. Wegner, Konrad Schindler
Probabilistic denoising diffusion models (DDMs) have set a new standard for 2D image generation.
1 code implementation • 13 Jun 2022 • Nikolai Kalischek, Nico Lang, Cécile Renier, Rodrigo Caye Daudt, Thomas Addoah, William Thompson, Wilma J. Blaser-Hart, Rachael Garrett, Konrad Schindler, Jan D. Wegner
C\^ote d'Ivoire and Ghana, the world's largest producers of cocoa, account for two thirds of the global cocoa production.
1 code implementation • 3 Jun 2022 • Andrés C. Rodríguez, Stefano D'Aronco, Rodrigo Caye Daudt, Jan D. Wegner, Konrad Schindler
Illustrations contained in field guides deliberately focus on discriminative properties of each species, and can serve as side information to transfer knowledge from seen to unseen bird species.
1 code implementation • CVPR 2022 • Riccardo de Lutio, Alexander Becker, Stefano D'Aronco, Stefania Russo, Jan D. Wegner, Konrad Schindler
With the decision to employ the source as a constraint rather than only as an input to the prediction, our method differs from state-of-the-art deep architectures for guided super-resolution, which produce targets that, when downsampled, will only approximately reproduce the source.
no code implementations • 7 Jun 2021 • Riccardo de Lutio, Yihang She, Stefano D'Aronco, Stefania Russo, Philipp Brun, Jan D. Wegner, Konrad Schindler
Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts.
no code implementations • 24 May 2021 • Andrés C. Rodríguez, Stefano D'Aronco, Konrad Schindler, Jan D. Wegner
To that end, we propose a new, active deep learning method to estimate oil palm density at large scale from Sentinel-2 satellite images, and apply it to generate complete maps for Malaysia and Indonesia.
no code implementations • 23 Mar 2020 • Ahmed Samy Nassar, Stefano D'Aronco, Sébastien Lefèvre, Jan D. Wegner
In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object.
2 code implementations • CVPR 2020 • Zan Gojcic, Caifa Zhou, Jan D. Wegner, Leonidas J. Guibas, Tolga Birdal
We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
no code implementations • ICCV 2019 • Ahmed Samy Nassar, Sebastien Lefevre, Jan D. Wegner
We propose to jointly learn multi-view geometry and warping between views of the same object instances for robust cross-view object detection.
2 code implementations • CVPR 2019 • Zan Gojcic, Caifa Zhou, Jan D. Wegner, Andreas Wieser
Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.
Ranked #4 on Point Cloud Registration on ETH (trained on 3DMatch)
no code implementations • 19 Sep 2018 • Andres C. Rodriguez, Jan D. Wegner
This task is hard due to the cluttered nature of scenes where different object categories occur.
1 code implementation • 31 Jan 2018 • Timo Hackel, Mikhail Usvyatsov, Silvano Galliani, Jan D. Wegner, Konrad Schindler
While CNNs naturally lend themselves to densely sampled data, and sophisticated implementations are available, they lack the ability to efficiently process sparse data.
no code implementations • ICCV 2017 • Maros Blaha, Mathias Rothermel, Martin R. Oswald, Torsten Sattler, Audrey Richard, Jan D. Wegner, Marc Pollefeys, Konrad Schindler
We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes.
1 code implementation • 12 Apr 2017 • Timo Hackel, Nikolay Savinov, Lubor Ladicky, Jan D. Wegner, Konrad Schindler, Marc Pollefeys
With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks.
no code implementations • CVPR 2016 • Jan D. Wegner, Steven Branson, David Hall, Konrad Schindler, Pietro Perona
The main technical challenge is combining test time information from multiple views of each geographic location (e. g., aerial and street views).
no code implementations • CVPR 2016 • Maros Blaha, Christoph Vogel, Audrey Richard, Jan D. Wegner, Thomas Pock, Konrad Schindler
We propose an adaptive multi-resolution formulation of semantic 3D reconstruction.
no code implementations • CVPR 2016 • Timo Hackel, Jan D. Wegner, Konrad Schindler
The contour scores serve as a basis to construct an overcomplete graph of candidate contours.
no code implementations • ISPRS annals of the photogrammetry, remote sensing and spatial information sciences 2016 • Timo Hackel, Jan D. Wegner, Konrad Schindler
We describe an effective and efficient method for point-wise semantic classification of 3D point clouds.
Ranked #17 on Semantic Segmentation on Semantic3D
no code implementations • CVPR 2013 • Jan D. Wegner, Javier A. Montoya-Zegarra, Konrad Schindler
The aim of this work is to extract the road network from aerial images.