Label-Efficient Point Cloud Segmentation with Active Learning

TL;DR

This paper introduces a simple, effective active learning strategy for 3D point cloud segmentation that uses a 2D grid and ensemble-based uncertainty estimation, achieving competitive results with less annotation effort.

Contribution

Proposes a novel, easy-to-implement region separation method and demonstrates its effectiveness across multiple datasets for point cloud segmentation.

Findings

Achieves comparable or better performance than complex state-of-the-art methods.

Shows that annotated area can be a more meaningful measure than number of points.

Validates approach on diverse datasets including urban city scans.

Abstract

Semantic segmentation of 3D point cloud data often comes with high annotation costs. Active learning automates the process of selecting which data to annotate, reducing the total amount of annotation needed to achieve satisfactory performance. Recent approaches to active learning for 3D point clouds are often based on sophisticated heuristics for both, splitting point clouds into annotatable regions and selecting the most beneficial for further neural network training. In this work, we propose a novel and easy-to-implement strategy to separate the point cloud into annotatable regions. In our approach, we utilize a 2D grid to subdivide the point cloud into columns. To identify the next data to be annotated, we employ a network ensemble to estimate the uncertainty in the network output. We evaluate our method on the S3DIS dataset, the Toronto-3D dataset, and a large-scale urban 3D point cloud of the city of Freiburg, which we labeled in parts manually. The extensive evaluation shows that our method yields performance on par with, or even better than, complex state-of-the-art methods on all datasets. Furthermore, we provide results suggesting that in the context of point clouds the annotated area can be a more meaningful measure for active learning algorithms than the number of annotated points.