title = {Fat {{Pad Cages}} for {{Facial Posing}}},
title = {Fat {{Pad Cages}} for {{Facial Posing}}},
author = {Colas, Adèle and Guiotte, Florent and Danieau, Fabien and Clerc, François Le and Avril, Quentin},
author = {Colas, Adèle and Guiotte, Florent and Danieau, Fabien and Clerc,
François Le and Avril, Quentin},
date = {2020-10-12},
date = {2020-10-12},
number = {arXiv:2010.05528},
number = {arXiv:2010.05528},
eprint = {2010.05528},
eprint = {2010.05528},
@ -10,84 +11,176 @@
doi = {10.48550/arXiv.2010.05528},
doi = {10.48550/arXiv.2010.05528},
url = {http://arxiv.org/abs/2010.05528},
url = {http://arxiv.org/abs/2010.05528},
urldate = {2023-01-25},
urldate = {2023-01-25},
abstract = {We introduce Fat Pad cages for posing facial meshes. It combines cage representation and facial anatomical elements, and enables users with no artistic skill to quickly sketch realistic facial expressions. The model relies on one or several cage(s) that deform(s) the mesh following the human fat pads map. We propose a new function to filter Green Coordinates using geodesic distances preventing global deformation while ensuring smooth deformations at the borders. Lips, nostrils and eyelids are processed slightly differently to allow folding up and opening. Cages are automatically created and fit any new unknown facial mesh. To validate our approach, we present a user study comparing our Fat Pad cages to regular Green Coordinates. Results show that Fat Pad cages bring a significant improvement in reproducing existing facial expressions.},
abstract = {We introduce Fat Pad cages for posing facial meshes. It combines
cage representation and facial anatomical elements, and enables
users with no artistic skill to quickly sketch realistic facial
expressions. The model relies on one or several cage(s) that
deform(s) the mesh following the human fat pads map. We propose a
new function to filter Green Coordinates using geodesic distances
preventing global deformation while ensuring smooth deformations at
the borders. Lips, nostrils and eyelids are processed slightly
differently to allow folding up and opening. Cages are
automatically created and fit any new unknown facial mesh. To
validate our approach, we present a user study comparing our Fat
Pad cages to regular Green Coordinates. Results show that Fat Pad
cages bring a significant improvement in reproducing existing
facial expressions.},
archiveprefix = {arXiv},
archiveprefix = {arXiv},
keywords = {mine},
keywords = {mine},
file = {/home/florent/.zotero/data/storage/EKVVUHR9/Colas et al. - 2020 - Fat Pad Cages for Facial Posing.pdf;/home/florent/.zotero/data/storage/42TANDRQ/2010.html},
file = {/home/florent/.zotero/data/storage/EKVVUHR9/Colas et al. - 2020 - Fat
title = {{{2D}}/{{3D}} Discretization of {{Lidar}} Point Clouds: {{Processing}} with Morphological Hierarchies and Deep Neural Networks},
title = {{{2D}}/{{3D}} Discretization of {{Lidar}} Point Clouds: {{Processing}
} with Morphological Hierarchies and Deep Neural Networks},
shorttitle = {{{2D}}/{{3D}} Discretization of {{Lidar}} Point Clouds},
shorttitle = {{{2D}}/{{3D}} Discretization of {{Lidar}} Point Clouds},
author = {Guiotte, Florent},
author = {Guiotte, Florent},
date = {2021-01-25},
date = {2021-01-25},
institution = {{Université Rennes 2}},
institution = {{Université Rennes 2}},
location = {{Rennes}},
location = {{Rennes}},
abstract = {This thesis evaluates the relevance of morphological hierarchies and deep neural networks for analysing LiDAR data by means of several discretization strategies. The quantity of data increases exponentially in coverage and resolution. However, actual datasets are not yet fully exploited due to the lack of efficient methodological tools for this specific type of data. Morphological structures are known to extract reliable multi-scale features while being extremely computationally efficient. In the mean time, the tremendous breakthrough of deep learning in computer vision has shaken up the remote sensing community. To this end we define and evaluate different discretization strategies of LiDAR data. In a first part, we re-organise the point clouds into 2D regular grids. We propose to derive several LiDAR features, trying to extract complete elevation description and spectral values along with LiDAR specific information. In a second part we re-organise the point clouds into 3D regular grids. The regular grids are sufficient to provide the neighboring context needed for the morphological hierarchies, and the proposed grids are also adapted to the input layers of state-of-the-art deep neural networks. The different methods are systematically validated in remote sensing scenarios.},
abstract = {This thesis evaluates the relevance of morphological hierarchies
and deep neural networks for analysing LiDAR data by means of
several discretization strategies. The quantity of data increases
exponentially in coverage and resolution. However, actual datasets
are not yet fully exploited due to the lack of efficient
methodological tools for this specific type of data. Morphological
structures are known to extract reliable multi-scale features while
being extremely computationally efficient. In the mean time, the
tremendous breakthrough of deep learning in computer vision has
shaken up the remote sensing community. To this end we define and
evaluate different discretization strategies of LiDAR data. In a
first part, we re-organise the point clouds into 2D regular grids.
We propose to derive several LiDAR features, trying to extract
complete elevation description and spectral values along with LiDAR
specific information. In a second part we re-organise the point
clouds into 3D regular grids. The regular grids are sufficient to
provide the neighboring context needed for the morphological
hierarchies, and the proposed grids are also adapted to the input
layers of state-of-the-art deep neural networks. The different
methods are systematically validated in remote sensing scenarios.},
bibtex_show = {true},
bibtex_show = {true},
hal_id = {tel-03385817},
hal_id = {tel-03385817},
hal_version = {v1},
hal_version = {v1},
langid = {english},
langid = {english},
pdf = {Guiotte - 2021 - 2D3D discretization of Lidar point clouds Proces.pdf},
pdf = {Guiotte - 2021 - 2D3D discretization of Lidar point clouds Proces.pdf},
keywords = {mine},
keywords = {mine},
file = {/home/florent/.zotero/data/storage/3AUGKHQS/Guiotte - 2021 - 2D3D discretization of Lidar point clouds Proces.pdf;/home/florent/.zotero/data/storage/ZBHU3Q2H/Guiotte - 2021 - 2D3D discretization of Lidar point clouds Proces.pdf}
abstract = {This paper deals with morphological characterization of un-structured 3D point clouds issued from LiDAR data. A large majority of studies first rasterize 3D point clouds onto regular 2D grids and then use standard 2D image processing tools for characterizing data. In this paper, we suggest instead to keep the 3D structure as long as possible in the process. To this end, as raw LiDAR point clouds are unstructured, we first propose some voxelization strategies and then extract some morphological features on voxel data. The results obtained with attribute filtering show the ability of this process to efficiently extract useful information .},
abstract = {This paper deals with morphological characterization of
un-structured 3D point clouds issued from LiDAR data. A large
majority of studies first rasterize 3D point clouds onto regular 2D
grids and then use standard 2D image processing tools for
characterizing data. In this paper, we suggest instead to keep the
3D structure as long as possible in the process. To this end, as
raw LiDAR point clouds are unstructured, we first propose some
voxelization strategies and then extract some morphological
features on voxel data. The results obtained with attribute
filtering show the ability of this process to efficiently extract
useful information .},
hal_id = {hal-02343890},
hal_id = {hal-02343890},
hal_version = {v1},
hal_version = {v1},
pdf = {https://hal.archives-ouvertes.fr/hal-02343890/file/ismm2019.pdf},
pdf = {https://hal.archives-ouvertes.fr/hal-02343890/file/ismm2019.pdf},
keywords = {mine},
keywords = {mine},
annotation = {00000},
annotation = {00000},
file = {/home/florent/.zotero/data/storage/M92S96E2/Guiotte et al. - 2019 - Attribute filtering of urban point clouds using ma.pdf}
file = {/home/florent/.zotero/data/storage/M92S96E2/Guiotte et al. - 2019 -
Attribute filtering of urban point clouds using ma.pdf},
}
}
@article{GuiotteFiltrage2019,
@article{GuiotteFiltrage2019,
title = {Filtrage et classification de nuage de points sur la base d'attributs morphologiques},
title = {Filtrage et classification de nuage de points sur la base d'attributs
morphologiques},
author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
date = {2019},
date = {2019},
journaltitle = {Journées francophones des jeunes chercheurs en vision par ordinateur},
journaltitle = {Journées francophones des jeunes chercheurs en vision par
ordinateur},
shortjournal = {ORASIS},
shortjournal = {ORASIS},
pages = {9},
pages = {9},
abstract = {Cet article traite de l’analyse de données LiDAR via la caractérisation morphologique des nuages de points qui en résultent. Tandis que la majorité de travaux effectuent en premier lieu une «rasterisation» (transformation du nuage de point en données 2D structurées en pixels) et utilisent ensuite des outils d’analyse d’images, nous proposons ici de garder le plus longtemps possible la structure 3D (en y calculant des caractéristiques) et de structurer les données le plus tard possible. En pratique, une étape de voxelisation des données brutes est opérée afin d’utiliser des outils mathématiques définis sur des volumes réguliers. Ensuite, nous utilisons des représentations hiérarchiques pour caractériser ces voxels. Pour illustrer les intérêts d’une telle approche, plusieurs applications sont proposées, notamment le débruitage, le filtrage et la classification des nuages de points},
abstract = {Cet article traite de l’analyse de données LiDAR via la
caractérisation morphologique des nuages de points qui en
résultent. Tandis que la majorité de travaux effectuent en premier
lieu une «rasterisation» (transformation du nuage de point en
données 2D structurées en pixels) et utilisent ensuite des outils
d’analyse d’images, nous proposons ici de garder le plus longtemps
possible la structure 3D (en y calculant des caractéristiques) et
de structurer les données le plus tard possible. En pratique, une
étape de voxelisation des données brutes est opérée afin d’utiliser
des outils mathématiques définis sur des volumes réguliers. Ensuite
, nous utilisons des représentations hiérarchiques pour
caractériser ces voxels. Pour illustrer les intérêts d’une telle
approche, plusieurs applications sont proposées, notamment le
débruitage, le filtrage et la classification des nuages de points},
hal_id = {hal-02343933},
hal_id = {hal-02343933},
langid = {french},
langid = {french},
keywords = {mine},
keywords = {mine},
annotation = {00000},
annotation = {00000},
file = {/home/florent/.zotero/data/storage/YVS8PZP6/Guiotte et al. - 2019 - Filtrage et classification de nuage de points sur .pdf}
file = {/home/florent/.zotero/data/storage/YVS8PZP6/Guiotte et al. - 2019 -
Filtrage et classification de nuage de points sur .pdf},
}
}
@article{GuiotteInteractive2020,
@article{GuiotteInteractive2020,
title = {Interactive {{Digital Terrain Model Analysis}} in {{Attribute Space}}},
title = {Interactive {{Digital Terrain Model Analysis}} in {{Attribute Space}}
author = {Guiotte, Florent and Etaix, Geoffroy and Lefèvre, Sébastien and Corpetti, Thomas},
},
author = {Guiotte, Florent and Etaix, Geoffroy and Lefèvre, Sébastien and
Corpetti, Thomas},
date = {2020},
date = {2020},
journaltitle = {International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
journaltitle = {International Archives of the Photogrammetry, Remote Sensing
and Spatial Information Sciences},
shortjournal = {ISPRS},
shortjournal = {ISPRS},
volume = {XLIII-B2-2020},
volume = {XLIII-B2-2020},
pages = {1203--1209},
pages = {1203--1209},
doi = {10.5194/isprs-archives-XLIII-B2-2020-1203-2020},
doi = {10.5194/isprs-archives-XLIII-B2-2020-1203-2020},
abstract = {The use of high-resolution digital terrain model derived from airborne LiDAR system becomes more and more prevalent. Effective multi-scale structure characterization is of crucial importance for various domains such as geosciences, archaeology and Earth observation. This paper deals with structure detection in large datasets with little or no prior knowledge. In a recent work, we have demonstrated the relevance of hierarchical representations to enhance the description of digital elevation models (Guiotte et al., 2019). In this paper, we proceed further and use the pattern spectrum, a multi-scale tool originating from mathematical morphology, further enhanced by hierarchical representations. The pattern spectra allow to globally and efficiently compute the distribution of size and shapes of the objects contained in a digital elevation model. The tree-based pattern spectra used in this paper allowed us to analyse and extract features of interest. We report experiments in a natural environment with two use cases, related to gold panning and dikes respectively. The process is fast enough to allow interactive analysis.},
abstract = {This paper evaluates rasterization strategies and the benefit of hierarchical representations, in particular attribute profiles, to classify urban scenes issued from multispectral LiDAR acquisitions. In recent years it has been found that rasterized LiDAR provides a reliable source of information on its own or for fusion with multispectral/hyperspectral imagery. However previous works using attribute profiles on LiDAR rely on elevation data only. Our approach focuses on several LiDAR features rasterized with multilevel description to produce precise land cover maps over urban areas. Our experimental results obtained with LiDAR data from university of Houston indicate good classification results for alternative rasters and even more when multilevel image descriptions are used.},
abstract = {This paper evaluates rasterization strategies and the benefit of
hierarchical representations, in particular attribute profiles, to
classify urban scenes issued from multispectral LiDAR acquisitions.
In recent years it has been found that rasterized LiDAR provides a
reliable source of information on its own or for fusion with
multispectral/hyperspectral imagery. However previous works using
attribute profiles on LiDAR rely on elevation data only. Our
approach focuses on several LiDAR features rasterized with
multilevel description to produce precise land cover maps over
urban areas. Our experimental results obtained with LiDAR data from
university of Houston indicate good classification results for
alternative rasters and even more when multilevel image
abstract = {LiDAR data are widely used in various domains related to geosciences (flow, erosion, rock deformations, etc.), computer graphics (3D reconstruction) or earth observation (detection of trees, roads, buildings, etc.). Because of the unstructured nature of remaining 3D points and because of the cost of acquisition, the LiDAR data processing is still challenging (few learning data, difficult spatial neighboring relationships, etc.). In practice, one can directly analyze the 3D points using feature extraction and then classify the points via machine learning techniques (Brodu, Lague, 2012, Niemeyer et al., 2014, Mallet et al., 2011). In addition, recent neural network developments have allowed precise point cloud segmentation, especially using the seminal pointnet network and its extensions (Qi et al., 2017a, Riegler et al., 2017). Other authors rather prefer to rasterize / voxelize the point cloud and use more conventional computers vision strategies to analyze structures (Lodha et al., 2006). In a recent work, we demonstrated that Digital Elevation Models (DEM) is reductive of the vertical component complexity describing objects in urban environments (Guiotte et al., 2020). These results highlighted the necessity to preserve the 3D structure of the point cloud as long as possible in the processing. In this paper, we therefore rely on ortho-waveforms to compute a land cover map. Ortho-waveforms are directly computed from the waveforms in a regular 3D grid. This method provides volumes somehow "similar" to hyperspectral data where each pixel is here associated with one ortho-waveform. Then, we exploit efficient neural networks adapted to the classification of hyperspectral data when few samples are available. Our results, obtained on the 2018 Data Fusion Contest dataset (DFC), demonstrate the efficiency of the approach.},
abstract = {LiDAR point clouds are receiving a growing interest in remote sensing as they provide rich information to be used independently or together with optical data sources such as aerial imagery. However, their non-structured and sparse nature make them difficult to handle, conversely to raw imagery for which many efficient tools are available. To overcome this specific nature of LiDAR point clouds, standard approaches often rely in converting the point cloud into a digital elevation model, represented as a 2D raster. Such a raster can then be used similarly as optical images, e.g. with 2D convolutional neural networks for semantic segmentation. In this letter, we show that LiDAR point clouds provide more information than only the DEM, and that considering alternative rasterization strategies helps to achieve better semantic segmentation results. We illustrate our findings on the IEEE DFC 2018 dataset.},
abstract = {LiDAR point clouds are receiving a growing interest in remote
sensing as they provide rich information to be used independently
or together with optical data sources such as aerial imagery.
However, their non-structured and sparse nature make them difficult
to handle, conversely to raw imagery for which many efficient tools
are available. To overcome this specific nature of LiDAR point
clouds, standard approaches often rely in converting the point
cloud into a digital elevation model, represented as a 2D raster.
Such a raster can then be used similarly as optical images, e.g.
with 2D convolutional neural networks for semantic segmentation. In
this letter, we show that LiDAR point clouds provide more
information than only the DEM, and that considering alternative
rasterization strategies helps to achieve better semantic
segmentation results. We illustrate our findings on the IEEE DFC
2018 dataset.},
hal_id = {hal-02399410},
hal_id = {hal-02399410},
hal_version = {v1},
hal_version = {v1},
pdf = {https://hal.archives-ouvertes.fr/hal-02399410/file/grsl.pdf},
pdf = {https://hal.archives-ouvertes.fr/hal-02399410/file/grsl.pdf},
file = {/home/florent/.zotero/data/storage/FEUUXDVN/Guiotte et al. - 2020 - Semantic segmentation of łd points clouds Rasteri.pdf}
file = {/home/florent/.zotero/data/storage/FEUUXDVN/Guiotte et al. - 2020 -
Semantic segmentation of łd points clouds Rasteri.pdf},
}
}
@article{GuiotteStrategies2019,
@article{GuiotteStrategies2019,
title = {Stratégies de rastérisation pour la classification de données LiDAR aéroportées par profils d'attributs morphologiques},
title = {Stratégies de rastérisation pour la classification de données LiDAR
aéroportées par profils d'attributs morphologiques},
author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
author = {Guiotte, Florent and Lefèvre, Sébastien and Corpetti, Thomas},
date = {2019},
date = {2019},
journaltitle = {Colloque GRETSI sur le Traitement du Signal et des Images},
journaltitle = {Colloque GRETSI sur le Traitement du Signal et des Images},
shortjournal = {GRETSI},
shortjournal = {GRETSI},
pages = {5},
pages = {5},
abstract = {Cet article traite de rastérisation par représentations hiérarchiques (en particulier via les profils d'attributs mor-phologiques) de nuages de points 3D. Lorsque ces données proviennent d'appareils LiDAR, il est fréquent de les rastériser pour fournir une carte d'élévation (exploitée seule ou combinée avec des images multi-ou hyperspectrales). Bien que certains travaux utilisent des profils d'attributs sur de telles données d'élévation, nous mettons ici l'accent sur plusieurs caractéristiques LiDAR rastérisées (liées aux échos, retours d'onde, etc.) et sur une description multi-échelle pour produire des cartes d'occupation du sol précises sur des zones urbaines. Nos résultats expérimentaux obtenus avec les données LiDAR de l'université de Houston indiquent de bons résultats de classification en exploitant nos rasters.},
abstract = {Cet article traite de rastérisation par représentations
hiérarchiques (en particulier via les profils d'attributs
mor-phologiques) de nuages de points 3D. Lorsque ces données
proviennent d'appareils LiDAR, il est fréquent de les rastériser
pour fournir une carte d'élévation (exploitée seule ou combinée
avec des images multi-ou hyperspectrales). Bien que certains
travaux utilisent des profils d'attributs sur de telles données
d'élévation, nous mettons ici l'accent sur plusieurs
caractéristiques LiDAR rastérisées (liées aux échos, retours d'onde
, etc.) et sur une description multi-échelle pour produire des
cartes d'occupation du sol précises sur des zones urbaines. Nos
résultats expérimentaux obtenus avec les données LiDAR de
l'université de Houston indiquent de bons résultats de
classification en exploitant nos rasters.},
bibtex_show = {true},
bibtex_show = {true},
hal_id = {hal-02343958},
hal_id = {hal-02343958},
langid = {french},
langid = {french},
keywords = {lidar,mine},
keywords = {lidar,mine},
annotation = {00000},
annotation = {00000},
file = {/home/florent/.zotero/data/storage/BMGP8AJA/Guiotte et al. - 2019 - Stratégies de rastérisation pour la classification.pdf}
file = {/home/florent/.zotero/data/storage/BMGP8AJA/Guiotte et al. - 2019 -
Stratégies de rastérisation pour la classification.pdf},
abstract = {This paper deals with strategies for LiDAR data analysis. While a large majority of studies first rasterize 3D point clouds onto regular 2D grids and then use 2D image processing tools for characterizing data, our work rather suggests to keep as long as possible the 3D structure by computing features on 3D data and rasterize later in the process. By this way, the vertical component is still taken into account. In practice, a voxelization step of raw data is performed in order to exploit mathematical tools defined on regular volumes. More precisely, we focus on attribute profiles that have been shown to be very efficient features to characterize remote sensing scenes. They require the computation of an underlying hierarchical structure (through a Max-Tree). Experimental results obtained on urban LiDAR data classification support the performances of this strategy compared with an early rasterization process.},
abstract = {This paper deals with strategies for LiDAR data analysis. While a
large majority of studies first rasterize 3D point clouds onto
regular 2D grids and then use 2D image processing tools for
characterizing data, our work rather suggests to keep as long as
possible the 3D structure by computing features on 3D data and
rasterize later in the process. By this way, the vertical component
is still taken into account. In practice, a voxelization step of
raw data is performed in order to exploit mathematical tools
defined on regular volumes. More precisely, we focus on attribute
profiles that have been shown to be very efficient features to
characterize remote sensing scenes. They require the computation of
an underlying hierarchical structure (through a Max-Tree).
Experimental results obtained on urban LiDAR data classification
support the performances of this strategy compared with an early
file = {/home/florent/.zotero/data/storage/65K47AUD/Guiotte et al. - 2019 - Voxel-based attribute profiles on LiDAR data for l.pdf}
file = {/home/florent/.zotero/data/storage/65K47AUD/Guiotte et al. - 2019 -
Voxel-based attribute profiles on LiDAR data for l.pdf},
}
}
@article{LeLearning2022,
@article{LeLearning2022,
title = {Learning {{Digital Terrain Models From Point Clouds}}: {{ALS2DTM Dataset}} and {{Rasterization-Based GAN}}},
title = {Learning {{Digital Terrain Models From Point Clouds}}: {{ALS2DTM
Dataset}} and {{Rasterization-Based GAN}}},
shorttitle = {Learning {{Digital Terrain Models From Point Clouds}}},
shorttitle = {Learning {{Digital Terrain Models From Point Clouds}}},
author = {Lê, Hoàng-Ân and Guiotte, Florent and Pham, Minh-Tan and Lefèvre, Sébastien and Corpetti, Thomas},
author = {Lê, Hoàng-Ân and Guiotte, Florent and Pham, Minh-Tan and Lefèvre,
Sébastien and Corpetti, Thomas},
date = {2022},
date = {2022},
journaltitle = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
journaltitle = {IEEE Journal of Selected Topics in Applied Earth Observations
and Remote Sensing},
shortjournal = {JSTARS},
shortjournal = {JSTARS},
volume = {15},
volume = {15},
eprint = {2206.03778},
eprint = {2206.03778},
@ -184,20 +374,43 @@
pages = {4980--4989},
pages = {4980--4989},
issn = {2151-1535},
issn = {2151-1535},
doi = {10.1109/JSTARS.2022.3182030},
doi = {10.1109/JSTARS.2022.3182030},
abstract = {Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging. This might be due to the lack of the dedicated large-scale annotated dataset and the data-structure discrepancy between point clouds and DTMs. To promote data-driven DTM extraction, this article collects from open sources a large-scale dataset of ALS point clouds and corresponding DTMs with various urban, forested, and mountainous scenes. A baseline method is proposed as the first attempt to train a deep neural network to extract DTMs directly from ALS point clouds via rasterization techniques, coined DeepTerRa. Extensive studies with well-established methods are performed to benchmark the dataset and analyze the challenges in learning to extract DTM from point clouds. The experimental results show the interest of the agnostic data-driven approach, with submetric error level compared to methods designed for DTM extraction. The data and source code are available online at https://lhoangan.github.io/deepterra/ for reproducibility and further similar research.},
abstract = {Despite the popularity of deep neural networks in various domains,
the extraction of digital terrain models (DTMs) from airborne laser
scanning (ALS) point clouds is still challenging. This might be due
to the lack of the dedicated large-scale annotated dataset and the
data-structure discrepancy between point clouds and DTMs. To
promote data-driven DTM extraction, this article collects from open
sources a large-scale dataset of ALS point clouds and corresponding
DTMs with various urban, forested, and mountainous scenes. A
baseline method is proposed as the first attempt to train a deep
neural network to extract DTMs directly from ALS point clouds via
rasterization techniques, coined DeepTerRa. Extensive studies with
well-established methods are performed to benchmark the dataset and
analyze the challenges in learning to extract DTM from point
clouds. The experimental results show the interest of the agnostic
data-driven approach, with submetric error level compared to
methods designed for DTM extraction. The data and source code are
available online at https://lhoangan.github.io/deepterra/ for
reproducibility and further similar research.},
archiveprefix = {arXiv},
archiveprefix = {arXiv},
eventtitle = {{{IEEE Journal}} of {{Selected Topics}} in {{Applied Earth Observations}} and {{Remote Sensing}}},
eventtitle = {{{IEEE Journal}} of {{Selected Topics}} in {{Applied Earth
file = {/home/florent/.zotero/data/storage/V7CAY453/Lê et al. - 2022 - Learning Digital Terrain Models From Point Clouds.pdf;/home/florent/.zotero/data/storage/BDA5G3YP/9794452.html}
file = {/home/florent/.zotero/data/storage/V7CAY453/Lê et al. - 2022 -
title = {Classification of {{Remote Sensing Data With Morphological Attribute Profiles}}: {{A}} Decade of Advances},
title = {Classification of {{Remote Sensing Data With Morphological Attribute
shorttitle = {Classification of {{Remote Sensing Data With Morphological Attribute Profiles}}},
Profiles}}: {{A}} Decade of Advances},
author = {Maia, Deise Santana and Pham, Minh-Tan and Aptoula, Erchan and Guiotte, Florent and Lefèvre, Sébastien},
shorttitle = {Classification of {{Remote Sensing Data With Morphological
Attribute Profiles}}},
author = {Maia, Deise Santana and Pham, Minh-Tan and Aptoula, Erchan and
Guiotte, Florent and Lefèvre, Sébastien},
date = {2021-09},
date = {2021-09},
journaltitle = {IEEE Geoscience and Remote Sensing Magazine},
journaltitle = {IEEE Geoscience and Remote Sensing Magazine},
shortjournal = {GRSM},
shortjournal = {GRSM},
@ -206,11 +419,21 @@
pages = {43--71},
pages = {43--71},
issn = {2168-6831},
issn = {2168-6831},
doi = {10.1109/MGRS.2021.3051859},
doi = {10.1109/MGRS.2021.3051859},
abstract = {Morphological attribute profiles (APs) are among the most prominent methods for spatial–spectral pixel analysis of remote sensing images. Since their introduction a decade ago to tackle land cover classification, many studies have been contributed to the state of the art, focusing not only on their application to a wider range of tasks but also on their performance improvement and extension to more complex Earth observation data.},
abstract = {Morphological attribute profiles (APs) are among the most
prominent methods for spatial–spectral pixel analysis of remote
sensing images. Since their introduction a decade ago to tackle
land cover classification, many studies have been contributed to
the state of the art, focusing not only on their application to a
wider range of tasks but also on their performance improvement and
extension to more complex Earth observation data.},
eventtitle = {{{IEEE Geoscience}} and {{Remote Sensing Magazine}}},
eventtitle = {{{IEEE Geoscience}} and {{Remote Sensing Magazine}}},
hal_id = {hal-03199357},
hal_id = {hal-03199357},
pdf = {https://hal.science/hal-03199357v1/document},
pdf = {https://hal.science/hal-03199357v1/document},
keywords = {attribute profiles,mine},
keywords = {attribute profiles,mine},
annotation = {00010},
annotation = {00010},
file = {/home/florent/.zotero/data/storage/6WDRKG9L/Maia et al. - 2021 - Classification of Remote Sensing Data With Morphol.pdf;/home/florent/.zotero/data/storage/ZHLN4422/Maia et al. - 2021 - Classification of Remote Sensing Data With Morphol.pdf;/home/florent/.zotero/data/storage/YASAD9GI/9366293.html}
file = {/home/florent/.zotero/data/storage/6WDRKG9L/Maia et al. - 2021 -
Classification of Remote Sensing Data With
Morphol.pdf;/home/florent/.zotero/data/storage/ZHLN4422/Maia et al. -
# the socials will be displayed in the order they are defined here
# the socials will be displayed in the order they are defined here
# for more information, please refer to the documentation of jekyll-socials plugin: https://github.com/george-gca/jekyll-socials
# for more information, please refer to the documentation of jekyll-socials plugin: https://github.com/george-gca/jekyll-socials
cv_pdf:/assets/pdf/example_pdf.pdf# path to your CV PDF file
cv_pdf:/assets/pdf/example_pdf.pdf# path to your CV PDF file
email:you@example.com# your email address
email:florent@guiotte.fr# your email address
inspirehep_id:1010907# Inspire HEP author ID
inspirehep_id:1010907# Inspire HEP author ID
rss_icon:true# comment this line to hide the RSS icon
rss_icon:false# comment this line to hide the RSS icon
scholar_userid:qc6CJjYAAAAJ# your Google Scholar ID
scholar_userid:QHs7TucAAAAJ# your Google Scholar ID
# wechat_qr: # filename of your wechat qr-code saved as an image (e.g., wechat-qr.png if saved to assets/img/wechat-qr.png)
# wechat_qr: # filename of your wechat qr-code saved as an image (e.g., wechat-qr.png if saved to assets/img/wechat-qr.png)
# whatsapp_number: # your WhatsApp number (full phone number in international format. Omit any zeroes, brackets, or dashes when adding the phone number in international format.)
# whatsapp_number: # your WhatsApp number (full phone number in international format. Omit any zeroes, brackets, or dashes when adding the phone number in international format.)
custom_social:# can be any name here other than the ones already defined in the jekyll-socials plugin
#custom_social: # can be any name here other than the ones already defined in the jekyll-socials plugin
logo:https://www.alberteinstein.com/wp-content/uploads/2024/03/cropped-favicon-192x192.png# can be png, svg, jpg
# logo: https://www.alberteinstein.com/wp-content/uploads/2024/03/cropped-favicon-192x192.png # can be png, svg, jpg
Announcements and news can be much longer than just quick inline posts. In fact, they can have all the features available for the standard blog posts. See below.
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#### Hipster list
<ul>
<li>brunch</li>
<li>fixie</li>
<li>raybans</li>
<li>messenger bag</li>
</ul>
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> We do not grow absolutely, chronologically. We grow sometimes in one dimension, and not in another, unevenly. We grow partially. We are relative. We are mature in one realm, childish in another.
> —Anais Nin
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