Abstract: In recent years,deep learning-based point cloud semantic segmentation has achieved remarkable success,but it heavily relies on a large amount of densely annotated point cloud data.In order to reduce the annotation cost,many weakly supervised learning methods have emerged,and active learning is one of them.It reduces the annotation cost by selecting a subset of the point cloud for annotation,but the current methods don't fully consider the connection between all points in the region when estimating the amount of regional information,and the previous diversity selection methods take a lot of time.To alleviate these issues,this paper proposes an active learning method for point cloud semantic segmentation based on dynamic balance and distance suppression.The method considers the connection between all points in the region by introducing regional inconsistency,and uses a dynamic balance strategy to adjust the importance of point-level uncertainty and regional inconsistency to measure the amount of regional information.In addition,a feature-normal distance suppression strategy is designed to select representative regions.This strategy uses a simpler method when considering the spatial structure between regions,which avoids redundant labeling by deleting adjacent similar regions,thereby improving the efficiency of diversity selection.Experimental results on the S3DIS and Semantic3D datasets show that the proposed framework demonstrates state-of-the-art performance and effectively reduces the annotation cost and diversity selection time.

Key words: Point cloud semantic segmentation, Active learning, Dynamic balance, Point-level uncertainty, Regional inconsistency, Distance suppression