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Wiersma, R.T. (author), Nasikun, A. (author), Eisemann, E. (author), Hildebrandt, K.A. (author)Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D~data. In this paper, we aim to construct anisotropic convolution layers that work directly on the surface derived from a point cloud. This is challenging because of the lack of a global...journal article 2022
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Classification of airborne laser scanning point cloud using point-based convolutional neural networkZhu, Jianfeng (author), Sui, Lichun (author), Zang, Y. (author), Zheng, He (author), Jiang, Wei (author), Zhong, Mianqing (author), Ma, Fei (author)In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be...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