A multi-granularity semisupervised active learning for point cloud semantic segmentation

Recent successes in point cloud semantic segmentation heavily rely on a large amount of annotated data. Furthermore, three-dimensional point cloud data are generally sparse and unorganized, and a frame of point cloud usually includes more than 100,000 points, which increases the difficulty of point cloud annotation. To reduce the annotation efforts, we propose a multi-granularity semisupervised active learning pipeline which aims to select representative, uncertain and diverse data to annotate. To better exploit annotating budget, we first leverage the conventional point cloud registration algorithm to develop a matching score function which is used to select a representative subset. And then we change the annotating units from a point cloud scan to segmented regions through two semisupervised methods. Subsequently, in each active selection step, segmented region information is calculated with two terms: softmax entropy and point cloud intensity, and the latter serves to encourage region diversity. Finally, to further reduce annotation effort, semisupervised learning is introduced to our pipeline to automatically select a portion of unlabeled segmented regions with high confidence and assign pseudolabels to them. Extensive experiments show that our approach greatly outperforms previous active learning methods, and we obtain the mean class intersection-over-union performance of 95% fully supervised learning with merely 3% of labeled data on SemanticKITTI dataset.