489 research outputs found
HoviTronBear
No full text<p> # Sequence: HoviTronBear <br>
This dataset "HoviTronBear" is provided by Yupeng XIE, Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB (Ecole polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium. <br>
Supported by the EU project HoviTron (Holographic Vision for Immersive Tele-Robotic OperatioN), Call identifier: H2020-ICT-2019-3, Grant Agreement: 951989.</p>
<p> # License: <br>
CC BY-NC-SA</p>
<p> # Terms of Use: <br>
Any kind of publication or report using this sequence should refer to the following references.</p>
<p>[1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "HoviTronBear", 2021.</p>
<p>@misc{xie_hovitronbear_2021,<br>
title = {{HoviTronBear} {Test} {Sequence}},<br>
author = {Xie, Yupeng and Fachada, Sarah and Bonatto, Daniele and Lafruit, Gauthier},<br>
month = July,<br>
year = {2021},<br>
doi = {10.5281/zenodo.5047464}<br>
}</p>
<p>[2] Xie, Yupeng and Fachada, Sarah and Bonatto, Daniele and Lafruit, Gauthier</p>
<p>@inproceedings{xie2021view,<br>
title={View Synthesis: LiDAR Camera versus Depth Estimation},<br>
author={Xie, Yupeng and Fachada, Sarah and Bonatto, Daniele and Lafruit, Gauthier},<br>
booktitle={International Conference on Computer Graphics, Visualization and Computer Vision 2021 (WSCG)},<br>
year={2021}<br>
}</p>
<p><br>
# Production:<br>
Laboratory of Image Synthesis and Analysis, LISA department, EPB, ULB.</p>
<p> # Content:<br>
This dataset contains:<br>
- Sequences:<br>
RGB images acquired by the Intel Realsense LiDAR L515 camera [1], which have been calibrated by software [2][3].<br>
<br>
- LiDAR_DepthMaps:<br>
Depth maps acquired by the Intel Realsense LiDAR L515 camera have been registered to Sequences and calibrated by software [2][3].<br>
<br>
- DERS_DepthMaps:<br>
High-accurate depth maps are estimated by DERS [4] (Depth Estimation Reference Software). The inputs are from Sequences. <br>
<br>
- cfgs/DERS:<br>
Configuration files for implementing the DERS. Please be aware of setting your own system path to reading these files.<br>
<br>
- cam_params.json:<br>
file in OMAF coordinates system (Camera position: X: forwards, Y: left, Z: up, Rotation: yaw, pitch, roll) [5]</p>
<p> # References and links:</p>
<p>[1] https://www.intelrealsense.com/lidar-camera-l515/</p>
<p>[2] https://github.com/ethz-asl/kalibr</p>
<p>[3] https://github.com/colmap/colmap</p>
<p>[4] S.Rogge et al. In: MPEG-I Depth Estimation Reference Software. 2019, pp. 1–6. DOI: 10 .1109/IC3D48390.2019.8975995.</p>
<p>[5] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.<br>
https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-format</p>
Transparent Magritte Test Sequence
No full textTransparent-Magritte sequence by LISA ULB
The test sequence "Transparent Magritte" is provided by Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB, ULB.
License:
CC BY-NC-SA
Terms of Use:
Anykind of publication or report using this sequence should refer to the following references.
[1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "Transparent Magritte Test Sequence", 2021.
@misc{fachada_transparent_2021,
title = {Transparent {Magritte} {Test} {Sequence}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
month = feb,
year = {2021},
doi = {10.5281/zenodo.4488243}
}
[2] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit, "Light Field Rendering for non-Lambertian Objects," presented at the Electronic Imaging, 2021.
@inproceedings{fachada_light_2021,
title = {Light {Field} {Rendering} for non-{Lambertian} {Objects}},
booktitle = {Electronic {Imaging}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
year = {2021}
}
Production
Laboratory of Image Synthesis and Analysis, LISA department, EPB, Universite Libre de Bruxelles, Belgium.
Content:
This dataset contains a test scene created and rendered with Blender [1] and the addon script [2] extended for Blender 2.8. We provide the Bblender file and the rendered scene.
The scene contains a transparent refractive torus rendered in a regular camera array of 21x21 cameras.
In addition to the 3D model, two folders are available:
- `centered_cameras` resolution of 1000x1000, the cameras are centered on the refractive torus.
- `parallel_cameras` resolution of 2000x2000, the cameras are parallel, with a principal point at the center of the image.
Each of these folders contains:
- a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],
- a `parameters.cfg` generated with [2],
- a `texture` folder containing the rendered views in png format,
- a `depth` folder containing the associated depth maps in exr format.
References and links:
[1] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.
[2] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,
https://github.com/lightfield-analysis/blender-addon
https://github.com/dbonattoj/blender-addon
[3] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.
https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-formatAcknoledgments:
This work was supported by
Les Fonds de la Recherche Scientifique - FNRS, Belgium, under Grant n°3679514$, ColibriH
The European Commision project n°951989 on Interactive Technologies, H2020-ICT-2019-3, Hovitron.
Sarah Fachada is a Research Fellow of the Fonds de la Recherche Scientifique - FNRS, Belgiu
ULB ToysTable
No full textULB ToysTable sequence by LISA ULB
The test sequence "ULB ToysTable" is provided by Daniele Bonatto, Sarah Fachada, Gauthier Lafruit, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.
License:
CC BY-NC-SA
Terms of Use:
Anykind of publication or report using this sequence should refer to the following references.
[1] Daniele Bonatto, Sarah Fachada, Gauthier Lafruit, "ULB ToysTable", 2021.
@misc{bonatto_toystable_2021,
title = {{ULB} {ToysTable}},
author = {Bonatto, Daniele and Fachada, Sarah and Lafruit, Gauthier},
month = feb,
year = {2021},
doi = {10.5281/zenodo.5055542}
}
[2] A. Schenkel, D. Bonatto, S. Fachada, H.-L. Guillaume, et G. Lafruit, « Natural Scenes Datasets for Exploration in 6DOF Navigation », in 2018 International Conference on 3D Immersion (IC3D), Brussels, Belgium, déc. 2018, p. 1-8. doi: 10.1109/IC3D.2018.8657865.
@inproceedings{schenkel_natural_b_2018,
address = {Brussels, Belgium},
title = {Natural {Scenes} {Datasets} for {Exploration} in {6DOF} {Navigation}},
isbn = {978-1-5386-7590-8},
url = {https://doi.org/10.1109/IC3D.2018.8657865},
doi = {10.1109/IC3D.2018.8657865},
language = {en},
urldate = {2019-04-11},
booktitle = {2018 {International} {Conference} on {3D} {Immersion} ({IC3D})},
publisher = {IEEE},
author = {Schenkel, Arnaud and Bonatto, Daniele and Fachada, Sarah and Guillaume, Henry-Louis and Lafruit, Gauthier},
month = dec,
year = {2018},
pages = {1--8}
}
[3] D. Bonatto, A. Schenkel, T. Lenertz, Y. Li, et G. Lafruit, « [MPEG-I Visual] ULB High Density 2D/3D Camera Array data set, version 2 [m41083] », in ISO/IEC JTC1/SC29/WG11 MPEG2017/M41083, Torino, Italy, juill. 2017.
@inproceedings{bonatto_mpeg-i_2017,
address = {Torino, Italy},
title = {[{MPEG}-{I} {Visual}] {ULB} {High} {Density} {2D}/{3D} {Camera} {Array} data set, version 2 [m41083]},
doi = {ISO/IEC JTC1/SC29/WG11 MPEG2017/M41083},
author = {Bonatto, Daniele and Schenkel, Arnaud and Lenertz, Tim and Li, Yan and Lafruit, Gauthier},
month = jul,
year = {2017}
}
Production:
Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Universite Libre de Bruxelles, Belgium.
Content:
This dataset contains a static test scene created using a robotic bench described in [3]. We provide RGB textures and their associated depth maps captured using a Microsoft Kinect v2. We also provide depth maps estimated using MPEG's Depth Estimation Reference Software (DERS) [5].
The scene contains a table with several toys, boxes, a chessboard and the datacolor Spydercheckr® 24. The pictures were taken in a controlled light environment. In a post-processing pass, the colors were corrected and the depth map undistorded and reprojected as described in [2] to match the RGB images.
The dataset contains two bands of regurarly spaced 5x5 images (Plane A) and 3x5 images (Plane B) respectively.
In addition to the images and their depth maps, an accurate camera calibration file is provided following the format of [4]. It was computed as described in [2].
The dataset contains:
- a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],
- a `textures` folder containing the rendered views in png format,
- a `depths_DERS` folder containing the associated depth maps in exr format.
References and links:
[4] S. Fachada, B. Kroon, D. Bonatto, B. Sonneveldt, et G. Lafruit, « Reference View Synthesizer (RVS) 2.0 manual, [N17759] », juill. 2018.
[5] S. Rogge and D. Bonatto and J. Sancho and R. Salvador and E. Juarez and A. Munteanu and G. Lafruit, "MPEG-I Depth Estimation Reference Software", in 2019 International Conference on 3D Immersion (IC3D), 2019
The interactions between visual appearance and conceptual knowledge in the acquisition of perceptual expertise
No full textResearch on object recognition often uses novel objects to prevent contributions from conceptual knowledge. This dissertation reveals the interactions of visual and conceptual properties in object recognition using novel objects, and the impact of the interactions on object learning and perceptual expertise, in terms of behavior (Chapter 2) and neural activity using fMRI (Chapter 3). The results reveal how shape and conceptual information interact to facilitate perception and determine object representations in the visual system. This research demonstrates the power of manipulating both visual and conceptual factors with artificial objects and novel concepts created out of lists of words. It opens the way for further experimentation and theoretical development with regards to how different types of information interact to determine object percepts and concepts
The Representational Foundations of Updating Object Locations
No full textThe goal of the experiments presented in this paper was to contribute to an ongoing debate in human spatial cognition research concerning the relative importance of dynamic egocentric and enduring allocentric representations for updating the locations of multiple objects. Several studies have demonstrated an increase in configuration error, which is a measure of the quality of the angular configurational knowledge of object locations, after disorientation. Based on the assumption that the fidelity of allocentric representations could not be affected by disorientation, those studies concluded that the observed increase in configuration error was evidence for the usage of dynamic egocentric representations in spatial updating. The experiments discussed in this paper challenge this conclusion and present evidence that supports the hypothesis that allocentric representations are the primary foundation of spatial updating. Two pilot experiments and Experiment 1 showed that performance on a tested heading was not determined by the amount of rotation needed to reach that heading, as predicted by the hypothesis that spatial updating is based on dynamic egocentric representations. Instead, performance was determined by the relationship between the tested heading and the walls of the surrounding room, as predicted by the hypothesis that spatial updating is based on allocentric representations that are specified with respect to reference directions that are intrinsic to the represented environment. Experiment 2 attempted to identify temporal and capacity limitations of egocentric updating that would have explained why no evidence for the usage of dynamic egocentric representations was observed in the earlier experiments. However, even when tracking a single object over the course of only a few seconds, no evidence in support of dynamic egocentric representations was apparent. The final experiment ruled out the possibility that the results of the previous experiments were caused by differences in the disparity between tested headings and the learning orientation
The interactions between visual appearance and conceptual knowledge in the acquisition of perceptual expertise
No full textMagritte Sphere
No full text# Magritte-Sphere sequence by LISA ULB
The test sequence "Magritte Sphere" is provided by Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.
# License:
CC BY-NC-SA
# Terms of Use:
Anykind of publication or report using this sequence should refer to the following references.
[1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "Magritte Sphere Test Sequence", 2021.
@misc{fachada_magritte_2021,
title = {{Magritte} {Sphere} {Test} {Sequence}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
month = feb,
year = {2021},
doi = {10.5281/zenodo.5048265}
}
[2] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit, "Light Field Rendering for non-Lambertian Objects," presented at the Electronic Imaging, 2021.
@inproceedings{fachada_light_2021,
title = {Light {Field} {Rendering} for non-{Lambertian} {Objects}},
booktitle = {Electronic {Imaging}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
year = {2021}
}
# Production:
Laboratory of Image Synthesis and Analysis, LISA department, EPB, Universite Libre de Bruxelles, Belgium.
# Content:
This dataset contains a test scene created and rendered with Blender [1] and the addon script [2] extended for Blender 2.8. We provide the Blender file and the rendered scene.
The scene contains a non-Lambertian (transparent-refractive (T) or mirror-specular (M)) sphere rendered in a regular camera array of 21x21 cameras.
In addition to the 3D model, we provide the rendered images : resolution of 2000x2000, the cameras are parallel, with a principal point at the center of the image.
The dataset contains:
- a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],
- a `parameters.cfg` generated with [2],
- a `texture_M` folder containing the rendered views in png format for the mirror object,
- a `texture_T` folder containing the rendered views in png format for the transparent object,
- a `mask` folder containing the mask indicating the sphere,
- a `depth` folder containing the associated depth maps in exr format.
# References and links:
[1] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.
[2] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,
https://github.com/lightfield-analysis/blender-addon
https://github.com/dbonattoj/blender-addon
[3] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.
https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-formatAcknowledgments:
This work was supported by:
Les Fonds de la Recherche Scientifique - FNRS, Belgium, under Grant n°3679514$, ColibriH,
The European Commision project n°951989 on Interactive Technologies, H2020-ICT-2019-3, Hovitron.
Sarah Fachada is a Research Fellow of the Fonds de la Recherche Scientifique - FNRS, Belgiu
Note on the monodromy conjecture for a space monomial curve with a plane semigroup
No full textRoughly speaking, the monodromy conjecture for a singularity states that every pole of its motivic Igusa zeta function induces an eigenvalue of its monodromy. In this note, we determine both the motivic Igusa zeta function and the eigenvalues of monodromy for a space monomial curve that appears as the special fiber of an equisingular family whose generic fiber is a plane branch. In particular, this yields a proof of the monodromy conjecture for such a curve.sponsorship: The first author is partially supported by MTM2016-76868-C2-2-P from the Departamento de Industria e Innovacion del Gobierno de Aragon and Fondo Social Europeo E22 17R Grupo Consolidado Algebra y Geometria, and by FQM-333 from Junta de Andalucia. The second author is partially supported by the LISA Project ANR-17-CE40-0023. The third author is partially supported by the Research Foundation - Flanders (FWO) project G.0792.18N. The fourth author is supported by a PhD Fellowship of the Research Foundation - Flanders (no. 71587). (Departamento de Industria e Innovacion del Gobierno de Aragon|MTM2016-76868-C2-2-P, Fondo Social Europeo Grupo Consolidado Algebra y Geometria|E22 17R, Junta de Andalucia|FQM-333, LISA Project|ANR-17-CE40-0023, Research Foundation - Flanders (FWO)|G.0792.18N, Research Foundation - Flanders|71587)status: Publishe
ComplexObjectMove
No full text<p><strong>ComplexObjectMove:</strong></p>
<p>This plenoptic sequence dataset "ComplexObjectMove" is provided by Eline Soetens, Gauthier Lafruit, Mehrdad Teratani members of LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium and Jonghoon Yim, Byeungwoo Jeon members of the Departement of Electrical and Computer Engineering, SKKU (Sungkyunkwan University), Korea.</p>
<p><strong>License:</strong></p>
<p>CC BY-NC-SA</p>
<p><strong>Terms of Use:</strong></p>
<p>Any kind of publication or report using this sequence should refer to the following reference :</p>
<p>Eline Soetens, Jonghoon Yim, Byeungwoo Jeon, Gauthier Lafruit, Mehrdad Teratani, "ComplexObjectMove", 2024.</p>
<p>@misc{soetens_ComplexObjectMove_2024, title = {ComplexObjectMove}}, author = {Soetens, Eline and Yim, Jonghoon and Jeon, Byeungwoo and Lafruit, Gauthier and Teratani, Mehrdad }, month = jan, year = {2024}, doi = {10.5281/zenodo.10569052} }</p>
<p><strong>Content:</strong></p>
<p>This sequence was captured using a Raytrix R8 plenoptic camera [1] equipped with a 35mm lens. The sequence was recorded for 10 seconds at 30 fps with the Raytrix software RxLive 5.0 [2]. Each frame has a 3840 x 2160 resolution. The scene contains blocks with a simple texture, a sphere with a complex texture, two toy cars, and a reflective unicorn statue placed on a moving board. The toy cars and the unicorn statue present a lot of specularities, thus both objects have a non-Lambertian texture. The camera is fixed and only the objects are moving in the scene.</p>
<p>Please find a detailed description of the content of each file below:</p>
<ul>
<li><code>ComplexObjectMove</code>: Contains the 300 frames of the sequences in PNG format.</li>
<li><code>R8-ComplexObjectMove_Calibration.xml</code>: Contains the calibration parameters of the plenoptic camera given by the Raytrix SDK.</li>
</ul>
<p><strong>Reference and links:</strong></p>
<p>[1] <a href="https://raytrix.de/products/" target="_blank" rel="noopener">https://raytrix.de/products/</a></p>
<p>[2] <a href="https://raytrix.de/downloads/" target="_blank" rel="noopener">https://raytrix.de/downloads/</a></p><p>This work was supported in part by the FER 2021 project (1060H000066-FAISAN), Belgium; in part by the Emile DEFAY 2021 project (4R00H000236), Belgium; and in part by the FER 2023 project (1060H000075), Belgium. This work was supported in part by Basic Science Research Program (RS-2023-00208453) through the National Research Foundation of Korea (NRF) and by the ICT Creative Consilience Program (IITP-2023-2020-0-01821) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation), both funded by the Ministry of Science and ICT, Korea.</p>
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