Classification of Point Cloud Scenes with Multiscale Voxel Deep Network

TL;DR

This paper introduces a multiscale voxel-based CNN for classifying 3D point cloud scenes, effectively handling urban and indoor environments, and achieves competitive results on the Semantic3D benchmark.

Contribution

It presents a novel CNN architecture that classifies points using only their positions in multi-scale neighborhoods, advancing scene point cloud classification.

Findings

Ranked second on Semantic3D benchmark

Outperforms previous non-regularized methods

Effective for urban and indoor scene classification

Abstract

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).