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Classification of Point Cloud Scenes with Multiscale Voxel Deep Network

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In this article we describe a new convolutional neural network (CNN) to classify 3D point clouds of urban or indoor 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. 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 a regularization step).

Xavier Roynard, Jean-Emmanuel Deschaud, Fran\c{c}ois Goulette• 2018

Related benchmarks

TaskDatasetResultRank
Semantic segmentationS3DIS (Area 5)
mIOU54.7
799
Semantic segmentationSemantic3D reduced-8 (test)
mIoU65.3
33
Semantic segmentationNPM3D
mIoU66.9
20
Semantic segmentationS3DIS (5th fold)
Mean IoU46.32
19
3D Scene SegmentationSemantic3D reduced-8 online benchmark
mIoU65.3
7
3D Scene SegmentationParis-Lille-3D online benchmark (test)
mIoU66.9
4
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