Point cloud instance segmentation based on attention mechanism KNN and ASIS module

A point cloud instance segmentation model with a k-nearest neighbors (KNN) module featuring attention mechanism and an improved associatively segmenting instances and semantics (ASIS) module was proposed to address the problems of discrete segmentation and insufficient feature utilization in traditional 3D convolution-based algorithms. The model took voxels as input and extracted point features through sparse convolution of 3D submanifolds. The KNN algorithm with attention mechanism was used for reorganizing the features in the semantic and instance feature space to alleviate the problem caused by the quantization error of extracted features. The reorganized semantic and instance features were correlated through the improved ASIS module to enhance the discrimination between point features. For semantic features and instance embedding, the softmax module and the meanshift algorithm were applied to obtain semantic and instance segmentation results respectively. The public S3DIS dataset was employed to validate the proposed model. The experimental results showed that the instance segmentation results of the proposed model achieved 53.1%, 57.1%, 65.2% and 52.8% in terms of mean coverage (mCoV), mean weighted coverage (mWCov), mean precision (mPrec) and mean recall (mRec) for the instance segmentation. The semantic segmentation achieved 61.7% and 88.1% respectively in terms of mean intersection over union (mIoU) and Over-all accuracy (Oacc) for the semantic segmentation. The ablation experiment verified the effectiveness of the proposed modules. © 2023 Zhejiang University. All rights reserved.