Visual　perception　is　an　effective　way　to　obtain　the　spatial　characteristics　of　wireless　channels　and　to　reduce　the　overhead　for　communications　system.　A　critical　problem　for　the　visual　assistance　is　that　the　communications　system　needs　to　match　the　radio　signal　with　the　visual　information　of　the　corresponding　user,　i.e.,　to　identify　the　visual　user　that　corresponds　to　the　target　radio　signal　from　all　the　environmental　objects.　In　this　paper,　we　propose　a　user　matching　method　for　environment　with　a　variable　number　of　objects.　Specifically,　we　apply　3D　detection　to　extract　all　the　environmental　objects　from　the　images　taken　by　multiple　cameras.　Then,　we　design　a　deep　neural　network　(DNN)　to　estimate　the　location　distribution　of　users　by　the　images　and　beam　pairs　at　multiple　moments,　and　thereby　identify　the　users　from　all　the　extracted　environmental　objects.　Moreover,　we　present　a　resource　allocation　method　based　on　the　taken　images　to　reduce　the　time　and　spectrum　overhead　compared　to　traditional　resource　allocation　methods.　Simulation　results　show　that　the　proposed　user　matching　method　outperforms　the　existing　methods,　and　the　proposed　resource　allocation　method　can　achieve　92%　transmission　rate　of　the　traditional　resource　allocation　method　but　with　the　time　and　spectrum　overhead　significantly　reduced.