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

Xavier Roynard (1) , Jean-Emmanuel Deschaud (1) , François Goulette (1)

1 CAOR - Centre de Robotique

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.

Mots clés: Deep Learning; Point Cloud; Classification; Segmentation

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Apprentissage [cs.LG]

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. ⟨hal-01763469v1⟩