Classification of Point Cloud for Road Scene Understanding with Multiscale Voxel Deep Network

Abstract : In this article we describe a new convolutional neural network (CNN) to classify 3D point clouds of urban scenes. Solutions are given to the problems encountered working on scene point clouds, and a network is described that allows for point classification using only the position of points in a multi-scale neighborhood. This network enables the classification of 3D point clouds of road scenes necessary for the creation of maps for autonomous vehicles such as HD-Maps. On the reduced-8 Semantic3D benchmark [Hackel et al., 2017], this network, ranked second, beats the state of the art of point classification methods (those not using an additional regularization step as CRF). Our network has also been tested on a new dataset of labeled urban 3D point clouds for semantic segmentation.
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Communication dans un congrès
PPNIV'2018, Oct 2018, Madrid, Spain. 〈https://project.inria.fr/ppniv18〉
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Contributeur : Xavier Roynard <>
Soumis le : mercredi 11 avril 2018 - 10:35:44
Dernière modification le : mardi 8 janvier 2019 - 01:23:20

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  • HAL Id : hal-01763469, version 1
  • ARXIV : 1804.03583

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Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette. Classification of Point Cloud for Road Scene Understanding with Multiscale Voxel Deep Network. PPNIV'2018, Oct 2018, Madrid, Spain. 〈https://project.inria.fr/ppniv18〉. 〈hal-01763469v1〉

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