Classification of Point Cloud Scenes with Multiscale Voxel Deep Network
About
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
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Semantic segmentation | S3DIS (Area 5) | mIOU54.7 | 799 | |
| Semantic segmentation | Semantic3D reduced-8 (test) | mIoU65.3 | 33 | |
| Semantic segmentation | NPM3D | mIoU66.9 | 20 | |
| Semantic segmentation | S3DIS (5th fold) | Mean IoU46.32 | 19 | |
| 3D Scene Segmentation | Semantic3D reduced-8 online benchmark | mIoU65.3 | 7 | |
| 3D Scene Segmentation | Paris-Lille-3D online benchmark (test) | mIoU66.9 | 4 |
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