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Intro IA, Collège de France, Yan Lecun
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Relevé LIDAR en intérieur et traitement du nuage de point
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3D point cloud classification is an important task with applications in robotics, augmented reality and urban planning. Recent advances in Machine Learning and Computer Vision have proven that complex real-world tasks require large training data sets for classifier training. At the same time, until now there were no data sets for 3D point cloud classification which would be sufficiently rich in both object representations and number of labelled points. For example, the well-known Oakland data set contains less than 2 million labelled points. Another popular data set, the NYU benchmark, provides only indoor scenes. Finally, both Sydney Urban Objects data set and the IQmulus & TerraMobilita Contest use a 3D Velodyne LIDAR mounted on a car which provides much lower point density than a static scanner. The same counts for the Vaihingen3D airborne benchmark.
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Geometrical models of trees in the field of computational forestry are nowadays referred to as Quantitative Structure Models (QSMs). The capability of those models exceeds pure volume estimation of trees. If the volume of a tree is multiplied with density values the above ground biomass (AGB) of a tree can be derived non destructively.
Instead of being limited to predict the AGB like statistical or voxel based approaches QSMs give also insight into internal biomass distributions of a tree.
The geometrical building blocks of such models are commonly cylinders fitted into terrrestrial laser scan point clouds.
SimpleTree is an open source tool to build QSMs from TLS clouds (SimpleTree homepage). The released version is based on PCL 1.8.0.
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National instrument propose une introduction à la programmation multi-coeur, de bonnes bases à connaître
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Vous ne vous rappelez plus d'un commande ! passez par ici
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Mise en oeuvre de PyTorch sur CUDA