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Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans / Romain Loiseau (2023)  

Public Titre : Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans Type de document : Article/Communication Auteurs : Romain Loiseau , Auteur ; Elliot Vincent, Auteur ; Mathieu Aubry, Auteur ; Loïc Landrieu , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Importance : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] information complexe [Termes IGN] scène 3D [Termes IGN] semis de points [Termes IGN] zone urbaine Résumé : (auteur) We propose an unsupervised method for parsing large 3D scans of real-world scenes into interpretable parts. Our goal is to provide a practical tool for analyzing 3D scenes with unique characteristics in the context of aerial surveying and mapping, without relying on application-specific user annotations. Our approach is based on a probabilistic reconstruction model that decomposes an input 3D point cloud into a small set of learned prototypical shapes. Our model provides an interpretable reconstruction of complex scenes and leads to relevant instance and semantic segmentations. To demonstrate the usefulness of our results, we introduce a novel dataset of seven diverse aerial LiDAR scans. We show that our method outperforms state-of-the-art unsupervised methods in terms of decomposition accuracy while remaining visually interpretable. Our method offers significant advantage over existing approaches, as it does not require any manual annotations, making it a practical and efficient tool for 3D scene analysis. Our code and dataset are available at https://imagine.enpc.fr/~loiseaur/learnable-earth-parser Numéro de notice : P2023-005 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Preprint nature-HAL : Préprint DOI : sans En ligne : https://hal.science/hal-04135416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103347

A model you can hear: Audio identification with playable prototypes / Romain Loiseau (2022)  

Public Titre : A model you can hear: Audio identification with playable prototypes Type de document : Article/Communication Auteurs : Romain Loiseau , Auteur ; Baptiste Bouvier, Auteur ; Yann Teytaut, Auteur ; Elliot Vincent, Auteur ; Mathieu Aubry, Auteur ; Loïc Landrieu , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2022 Projets : READY3D / Landrieu, Loïc Conférence : ISMIR 2022, International Society for Music Information Retrieval Conference 04/12/2022 08/12/2022 Bengaluru Inde Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] apprentissage automatique [Termes IGN] bruit (audition) [Termes IGN] onde acoustique [Termes IGN] prototype Résumé : (auteur) Machine learning techniques have proved useful for classifying and analyzing audio content. However, recent methods typically rely on abstract and high-dimensional representations that are difficult to interpret. Inspired by transformation-invariant approaches developed for image and 3D data, we propose an audio identification model based on learnable spectral prototypes. Equipped with dedicated transformation networks, these prototypes can be used to cluster and classify input audio samples from large collections of sounds. Our model can be trained with or without supervision and reaches state-of-the-art results for speaker and instrument identification, while remaining easily interpretable. The code is available at https://github.com/romainloiseau/a-model-you-can-hear Numéro de notice : P2022-006 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : INFORMATIQUE Nature : Preprint nature-HAL : Préprint DOI : 10.48550/arXiv.2208.03311 En ligne : https://doi.org/10.48550/arXiv.2208.03311 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101330