no code implementations • 22 Sep 2023 • Jonathan Sauder, Guilhem Banc-Prandi, Anders Meibom, Devis Tuia
This paper presents a new paradigm for mapping underwater environments from ego-motion video, unifying 3D mapping systems that use machine learning to adapt to challenging conditions under water, combined with a modern approach for semantic segmentation of images.
1 code implementation • 7 Feb 2022 • Jonathan Sauder, Martin Genzel, Peter Jung
Countless signal processing applications include the reconstruction of signals from few indirect linear measurements.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Jonathan Sauder, Martin Genzel, Peter Jung
Countless signal processing applications include the reconstruction of an unknown signal from very few indirect linear measurements.
no code implementations • 23 Oct 2020 • Freya Behrens, Jonathan Sauder, Peter Jung
It is well-established that many iterative sparse reconstruction algorithms such as ISTA can be unrolled to yield a learnable neural network for improved empirical performance.
1 code implementation • ICLR 2021 • Freya Behrens, Jonathan Sauder, Peter Jung
A prime example is learned ISTA (LISTA) where weights, step sizes and thresholds are learned from training data.
no code implementations • LREC 2020 • Jonathan Sauder, Ting Hu, Xiaoyin Che, Goncalo Mordido, Haojin Yang, Christoph Meinel
Recently, various approaches with Generative Adversarial Nets (GANs) have also been proposed.
no code implementations • NeurIPS 2019 • Jonathan Sauder, Bjarne Sievers
Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation.
Ranked #10 on 3D Point Cloud Linear Classification on ModelNet40
3D Point Cloud Linear Classification General Classification +4