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Mohamed Boussaha

Mohamed Boussaha
French mapping agency - IGN

PhD

About

4
Publications
1,301
Reads
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158
Citations
Citations since 2017
4 Research Items
158 Citations
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201720182019202020212022202301020304050
201720182019202020212022202301020304050

Publications

Publications (4)
Preprint
Full-text available
We present a fully-supervized method for learning to segment data structured by an adjacency graph. We introduce the graph-structured contrastive loss, a loss function structured by a ground truth segmentation. It promotes learning vertex embeddings which are homogeneous within desired segments, and have high contrast at their interface. Thus, comp...
Conference Paper
Full-text available
We propose a new supervized learning framework for oversegmenting 3D point clouds into superpoints. We cast this problem as learning deep embeddings of the local geometry and radiometry of 3D points, such that the border of objects presents high contrasts. The embeddings are computed using a lightweight neural network operating on the points' local...
Preprint
Full-text available
We propose a new supervized learning framework for oversegmenting 3D point clouds into superpoints. We cast this problem as learning deep embeddings of the local geometry and radiometry of 3D points, such that the border of objects presents high contrasts. The embeddings are computed using a lightweight neural network operating on the points' local...
Article
Full-text available
The representation of 3D geometric and photometric information of the real world is one of the most challenging and extensively studied research topics in the photogrammetry and robotics communities. In this paper, we present a fully automatic framework for 3D high quality large scale urban texture mapping using oriented images and LiDAR scans acqu...

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