典型文献
Neighborhood co-occurrence modeling in 3D point cloud segmentation
文献摘要：
A significant performance boost has been achieved in point cloud semantic segmentation by utilization of the encoder-decoder architecture and novel convolution operations for point clouds.However,co-occurrence relationships within a local region which can directly influence segmentation results are usually ignored by current works.In this paper,we propose a neighborhood co-occurrence matrix(NCM)to model local co-occurrence relationships in a point cloud.We generate target NCM and prediction NCM from semantic labels and a prediction map respectively.Then,Kullback-Leibler(KL)divergence is used to maximize the similarity between the target and prediction NCMs to learn the co-occurrence relationship.Moreover,for large scenes where the NCMs for a sampled point cloud and the whole scene differ greatly,we introduce a reverse form of KL divergence which can better handle the difference to supervise the prediction NCMs.We integrate our method into an existing backbone and conduct comprehensive experiments on three datasets:Semantic3D for outdoor space segmentation,and S3DIS and ScanNet v2 for indoor scene segmentation.Results indicate that our method can significantly improve upon the backbone and outperform many leading competitors.
文献关键词：
中图分类号：
作者姓名：
Jingyu Gong;Zhou Ye;Lizhuang Ma
作者机构：
Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Shanghai CLS Fintech Co.,LTD,Shanghai 200030,China;MoE Key Lab of Artificial Intelligence,Shanghai Jiao Tong University,Shanghai 200240,China
文献出处：
引用格式：
[1]Jingyu Gong;Zhou Ye;Lizhuang Ma-.Neighborhood co-occurrence modeling in 3D point cloud segmentation)[J].计算可视媒体（英文）,2022(02):303-315
A类：
NCMs
B类：
Neighborhood,occurrence,modeling,point,segmentation,performance,boost,has,been,achieved,semantic,by,utilization,encoder,decoder,architecture,novel,convolution,operations,clouds,However,relationships,within,local,region,which,directly,influence,results,are,usually,ignored,current,works,In,this,paper,propose,neighborhood,matrix,We,generate,target,prediction,from,labels,map,respectively,Then,Kullback,Leibler,KL,divergence,used,maximize,similarity,between,learn,Moreover,large,scenes,where,sampled,whole,greatly,introduce,reverse,better,handle,difference,supervise,integrate,our,method,into,existing,backbone,conduct,comprehensive,experiments,three,datasets,Semantic3D,outdoor,space,S3DIS,ScanNet,v2,indoor,Results,indicate,that,significantly,improve,upon,outperform,many,leading,competitors
AB值：
0.583464
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
