With　the　rise　of　immersive　media　applications　such　as　digital　museums,　virtual　reality,　and　interactive　exhibitions,　point　clouds,　as　a　three-dimensional　data　storage　format,　have　gained　increasingly　widespread　attention.　The　massive　data　volume　of　point　clouds　imposes　extremely　high　requirements　on　transmission　bandwidth　in　the　above　applications,　gradually　becoming　a　bottleneck　for　immersive　media　applications.　Although　existing　learning-based　point　cloud　compression　methods　have　achieved　specific　successes　in　compression　efficiency　by　mining　the　spatial　redundancy　of　their　local　structural　features,　these　methods　often　overlook　the　intrinsic　connections　between　point　cloud　data　and　other　modality　data　(such　as　image　modality),　thereby　limiting　further　improvements　in　compression　efficiency.　To　address　the　limitation,　we　innovatively　propose　a　view-guided　learned　point　cloud　geometry　compression　scheme,　namely　ViewPCGC.　We　adopt　a　novel　self-attention　mechanism　and　cross-modality　attention　mechanism　based　on　sparse　convolution　to　align　the　modality　features　of　the　point　cloud　and　the　view　image,　removing　view　redundancy　through　Modality　Redundancy　Removal　Module　(MRRM).　Simultaneously,　side　information　of　the　view　image　is　introduced　into　the　Conditional　Checkboard　Entropy　Model　(CCEM),　significantly　enhancing　the　accuracy　of　the　probability　density　function　estimation　for　point　cloud　geometry.　In　addition,　we　design　a　View-Guided　Quality　Enhancement　Module　(VG-QEM)　in　the　decoder,　utilizing　the　contour　information　of　the　point　cloud　in　the　view　image　to　supplement　reconstruction　details.　The　superior　experimental　performance　demonstrates　the　effectiveness　of　our　method.　Compared　to　the　state-of-the-art　point　cloud　geometry　compression　methods,　ViewPCGC　exhibits　an　average　performance　gain　exceeding　10%　on　D1-PSNR　metric.　©　2024　ACM.