Semantic instance joint segmentation network for indoor scene of 3D point cloud
For the problem that the point cloud segmentation network can not realize high-precision segmentation in complex indoor scenarios,this paper designs a joint semantic-instance segmentation network based on deep learning,which can simultaneously complete semantic segmentation and instance segmentation of 3D point cloud data.It mainly includes the multi-task learning backbone network,the feature fusion module and the semantic feature joint instance module.The feature fusion module fuses multiple network layers through hop connection,and fuses the features of two tasks at different levels respectively,so as to strengthen the integration of information contained in the data by the network.The dataset of large indoor scene S3DIS and the component segmentation dataset ShapeNet were selected for comparative experiments.The experimental results show that the overall accuracy of semantic segmentation of the network in the data set S3DIS is 86.5%,the intersection ratio of semantic segmentation categories in the data set ShapeNet is 83.1%,and the average accuracy of instance segmentation in the data set S3DIS is 60.8%.The semantic instance feature combination module increases the discriminant features of semantics and instances through multi-task feature combination,and improves the accuracy of semantic segmentation and instance segmentation of point clouds.
3D point cloudindoor scenesemantic segmentationinstance segmentationmulti-task learningfeature fusionjoint segmentation
国家科技期刊平台
NSTL
万方数据
维普
