Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Vallet, Bruno"'
Current stereo-vision pipelines produce high accuracy 3D reconstruction when using multiple pairs or triplets of satellite images. However, these pipelines are sensitive to the changes between images that can occur as a result of multi-date acquisiti
Externí odkaz:
http://arxiv.org/abs/2403.18711
Publikováno v:
International Journal of Applied Earth Observation and Geoinformation, 128(2024)
Dense matching is crucial for 3D scene reconstruction since it enables the recovery of scene 3D geometry from image acquisition. Deep Learning (DL)-based methods have shown effectiveness in the special case of epipolar stereo disparity estimation in
Externí odkaz:
http://arxiv.org/abs/2402.12522
Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in
Externí odkaz:
http://arxiv.org/abs/2303.07182
Autor:
Grzeczkowicz, Grégoire, Vallet, Bruno
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2-2022, 2022, 177-184
Textured meshes are becoming an increasingly popular representation combining the 3D geometry and radiometry of real scenes. However, semantic segmentation algorithms for urban mesh have been little investigated and do not exploit all radiometric inf
Externí odkaz:
http://arxiv.org/abs/2302.10635
We present a comprehensive survey and benchmark of both traditional and learning-based methods for surface reconstruction from point clouds. This task is particularly challenging for real-world acquisitions due to factors like noise, outliers, non-un
Externí odkaz:
http://arxiv.org/abs/2301.13656
Publikováno v:
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5575-5584. 2022
Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clouds by processing each modality with a dedicated network and projecting learned 2D features onto 3D points. Merging large-scale point clouds and image
Externí odkaz:
http://arxiv.org/abs/2204.07548
Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations. Sensor visibility, however, holds meaningful information regarding space occupancy and surface orientation. In thi
Externí odkaz:
http://arxiv.org/abs/2202.01810
Publikováno v:
Computer Graphics Forum 2021
We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds. Our approach can cope with the scale and variety of point cloud defects encountered in real-life Multi-View Stereo (MVS)
Externí odkaz:
http://arxiv.org/abs/2107.06130
Autor:
Guinard, Stephane, Vallet, Bruno
We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our method uses the inherent topology of the MLS sensor to define a spatial adjacenc
Externí odkaz:
http://arxiv.org/abs/1804.04001
Autor:
Guinard, Stephane, Vallet, Bruno
We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local
Externí odkaz:
http://arxiv.org/abs/1802.07487