Papers › Classification of Point Cloud Scenes with Multiscale Voxel Deep Network
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network
Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette
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).
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | Semantic3D | MSDeepVoxNet | mIoU | 65.3% | #11 of 17 | Archive leaderboard | report |
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