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Abstract
This paper presents a new deep-learning-based method for 3D Point Cloud Semantic Segmentation specifically designed for processing real-world LIDAR railway scenes. The new approach relies on the use of spatial local point cloud transformations for convolutional learning. These transformations allow an increased robustness to varying point cloud densities while preserving metric information and a sufficient descriptive ability. The resulting performances are illustrated with results on railway data from two distinct LIDAR point cloud datasets acquired in industrial settings. The quality of the extraction of useful information for maintenance operations and topological analysis is pointed together with a noticeable robustness to point cloud variations in distribution and point redundancy.
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1 SNCF Réseau – DGII TTD, 9 Avenue François Mitterand, 93210 Saint-Denis, France; SNCF Réseau – DGII TTD, 9 Avenue François Mitterand, 93210 Saint-Denis, France; ONERA – DTIS, 6 chemin de la Vauve aux Granges, 91120 Palaiseau, France
2 ONERA – DTIS, 6 chemin de la Vauve aux Granges, 91120 Palaiseau, France; ONERA – DTIS, 6 chemin de la Vauve aux Granges, 91120 Palaiseau, France
3 SNCF Réseau – DGII TTD, 9 Avenue François Mitterand, 93210 Saint-Denis, France; SNCF Réseau – DGII TTD, 9 Avenue François Mitterand, 93210 Saint-Denis, France