Search Results for author: Jan D. Wegner

Found 21 papers, 7 papers with code

GeoGraph: Graph-based multi-view object detection with geometric cues end-to-end

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.

object-detection Object Detection

Tetrahedral Diffusion Models for 3D Shape Generation

no code implementations23 Nov 2022 Nikolai Kalischek, Torben Peters, Jan D. Wegner, Konrad Schindler

Recently, probabilistic denoising diffusion models (DDMs) have greatly advanced the generative power of neural networks.

3D Shape Generation Denoising +1

BiasBed -- Rigorous Texture Bias Evaluation

1 code implementation23 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.

Model Selection

Zero-Shot Bird Species Recognition by Learning from Field Guides

1 code implementation3 Jun 2022 Andrés C. Rodríguez, Stefano D'Aronco, Rodrigo Caye Daudt, Jan D. Wegner, Konrad Schindler

The illustrations contained in field guides deliberately focus on discriminative properties of a species, and can serve as side information to transfer knowledge from seen to unseen classes.

Generalized Zero-Shot Learning

Learning Graph Regularisation for Guided Super-Resolution

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.

Super-Resolution

Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs

no code implementations7 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.

Multimodal Deep Learning

Mapping oil palm density at country scale: An active learning approach

no code implementations24 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.

Active Learning Density Estimation

GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end

no code implementations23 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.

object-detection Object Detection

Simultaneous multi-view instance detection with learned geometric soft-constraints

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.

object-detection Object Detection

Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in Convolutional Networks

1 code implementation31 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.

Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark

1 code implementation12 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.

3D Point Cloud Classification Classification +5

Cataloging Public Objects Using Aerial and Street-Level Images - Urban Trees

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).

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