Research On Point Cloud Semantic Segmentation And Instance Segmentation Technology Based On Indoor Scene

Indoor scene understanding based on 3D point cloud data is an important research content in the field of computer vision.It refers to the direct processing of 3D point cloud data and the segmentation of scene semantic level and instance level.With the continuous improvement of the performance of three-dimensional sensor equipment,the acquisition of point cloud data is becoming easier.Effective point cloud segmentation is the basis of high-level vision tasks such as automatic driving,machine vision and remote sensing mapping,which has a broad application prospect.The main work of this paper is as follows:(2)In this paper,we propose a point cloud semantic segmentation technique based on a feature selection module.Existing point cloud semantic segmentation methods ignore the dependency between embedding feature channels.Hence,in this paper,a feature selection module is designed to compress and weight the feature channels through self-attention mechanism,so as to select the feature channels with strong semantic correlation.At the same time,this module is embedded into the Point Net-based network model to form a new network structure.The resulting method is tested on S3 DIS data set and Scan Net data set respectively,which proves the effectiveness of the method.(3)Aiming at the task of point cloud instance segmentation,this paper proposes an instance segmentation network based on direction coding.First,in order to obtain the information of multiple directions of point cloud,a direction coding unit is designed.It convolutes the information of eight adjacent directions along the X,Y,Z direction.To perceive a multi-scale feature expression of point cloud,the results are entered into a direction coding module to stack multiple direction coding units with different scales.Second,the direction coding module is combined with the SA module and the FP module in Point Net + +,then we design the feature extraction network.Third,a Double-Triplet loss function is designed to optimize the segmentation results.Through the integration of these three parts,an example segmentation network based on the direction coding module is designed,and experiments are carried out on the S3 DIS data set and the NYUV2 data set respectively.The results show that the method can improve the accuracy of the instance segmentation and the semantic segmentation at the same time.