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 \url{https://github.com/nerminsamet/seedal}.

翻译：我们提出 SeedAL，一种为高效注释用于语义分割的3D点云而播种主动学习的方法。主动学习（AL）迭代地选择相关数据子集在给定预算内进行注释，但需要数据集的第一个子集（即“种子”）已被注释，以估算注释其他数据子集的效益。我们首先展示了种子的选择会显著影响许多AL方法的性能。然后，我们提出了一种自动构建种子以确保AL获得良好性能的方法。假设点云的图像是可用的（这很常见），我们的方法依赖强大的无监督图像特征来衡量点云的多样性。它通过在一个注释预算下优化多样性来选择用于种子的点云，这可以通过求解一个线性优化问题来完成。我们的实验证明了我们的方法在 S3DIS 和 SemanticKitti 数据集上相比于随机播种和现有方法的有效性。代码可在 \url{https://github.com/nerminsamet/seedal} 获取。