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Nurunnabi, A. (author), Teferle, F. N. (author), Li, J. (author), Lindenbergh, R.C. (author), Parvaz, S. (author)
Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL...
journal article 2021
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Soilán, M. (author), Lindenbergh, R.C. (author), Riveiro, B. (author), Sánchez-Rodríguez, A. (author)
During the last couple of years, there has been an increased interest to develop new deep learning networks specifically for processing 3D point cloud data. In that context, this work intends to expand the applicability of one of these networks, PointNet, from the semantic segmentation of indoor scenes, to outdoor point clouds acquired with...
journal article 2019