We propose SeedAL, a method to seed active learning for efficient annotation of 3D point clouds for semantic segmentation. Active Learning (AL) iteratively selects relevant data fractions to annotate within a given budget, but requires a first fraction of the dataset (a 'seed') to be already annotated to estimate the benefit of annotating other data fractions. We first show that the choice of the seed can significantly affect the performance of many AL methods. We then propose a method for automatically constructing a seed that will ensure good performance for AL. Assuming that images of the point clouds are available, which is common, our method relies on powerful unsupervised image features to measure the diversity of the point clouds. It selects the point clouds for the seed by optimizing the diversity under an annotation budget, which can be done by solving a linear optimization problem. Our experiments demonstrate the effectiveness of our approach compared to random seeding and existing methods on both the S3DIS and SemanticKitti datasets. Code is available at https://github.com/nerminsamet/seedal.

翻译：我们提出SeedAL方法，这是一种为高效标注3D点云语义分割而设计的主动学习播种方法。主动学习迭代地在给定预算内选择相关数据子集进行标注，但需要已标注的第一个数据子集（即“种子”）来估算标注其他数据子集的收益。我们首先证明种子选择会显著影响许多主动学习方法的性能。随后提出一种自动构建种子以确保主动学习良好性能的方法。假设点云的图像可用（这通常是常见情况），我们的方法利用强大的无监督图像特征来度量点云的多样性。它通过优化标注预算下的多样性来选择种子点云，这可通过求解线性优化问题实现。在S3DIS和SemanticKitti数据集上的实验表明，与随机播种和现有方法相比，我们的方法具有有效性。代码地址：https://github.com/nerminsamet/seedal。