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.
Abstract (translated)
三维点云数据的语义分割通常伴随着高昂的数据标注成本。主动学习通过自动化选择需要标注的数据的过程，从而减少了实现满意性能所需的总标注量。最近针对3D点云的主动学习方法经常基于复杂的启发式算法来划分点云成可注释区域以及选择对后续神经网络训练最有益的数据。在这项工作中，我们提出了一种新颖且易于实施的战略，用于将点云划分为可注释区域。在我们的方法中，我们利用一个二维网格将点云划分为列。为了确定下一次要标注的数据，我们采用了一个网络集成来估计网络输出的不确定性。 我们在S3DIS数据集、多伦多-3D数据集以及弗赖堡市的大规模城市三维点云上评估了我们的方法。这些大规模数据集中的一部分是我们手动标记的。详细的评估表明，我们的方法在所有数据集上的表现都与复杂且最先进的方法相当，甚至更好。 此外，我们还提供了结果，显示在点云场景中，标注区域面积可以作为主动学习算法更有意义的度量标准，而不是标注点的数量。
URL
https://arxiv.org/abs/2512.05759
