A Point Cloud Image Segmentation Method for Indoor Determining Objects
Aiming at the application scenario of determining the target point cloud image segmentation in the room,at the se-mantic segmentation level,the PointNet++network is improved.In order to improve the accuracy and efficiency of down sampling,importance sampling is used to replace the farthest point sampling.To improve the feature extraction of the sparse area of the point cloud,threshold for feature extraction of the bounding ball is added.At the instance segmentation level,in order to effectively seg-ment the overlapping and interlaced point cloud images of multiple instances of the same type,on the basis of DBSCAN coarse clus-tering,each instance is separated one by one using the minimum cut algorithm.Experimental results show that at the level of seman-tic segmentation,the algorithm in this paper is superior to PointNet++in segmentation accuracy,training time-consuming,and seg-mentation performance in sparse point cloud regions.At the instance segmentation level,the total accuracy of the algorithm in this paper is 6.04%higher than that of the Jsnet network.
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