# Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark

## TL;DR

This paper introduces a large-scale, densely labeled 3D point cloud dataset with over four billion points, facilitating deep learning advancements in 3D scene understanding and semantic labeling.

## Contribution

It provides a new extensive benchmark dataset for 3D point cloud classification, enabling deep learning methods to improve and generalize in 3D semantic segmentation tasks.

## Key findings

- Deep CNNs show remarkable performance improvements on the benchmark.
- The dataset offers more dense and complete point clouds than previous datasets.
- Initial submissions indicate rapid progress in 3D deep learning methods.

## Abstract

This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.