Latent　diffusion　models　have　achieved　significant　success　in　point　cloud　generation　recently,　where　the　diffusion　process　is　constructed　under　a　low-dimensional　but　efficient　latent　space.　However,　existing　methods　usually　overlook　the　differences　between　consistency　information　and　offset　information　in　the　point　clouds,　leading　to　difficulty　in　accurately　learning　both　the　overall　shape　and　the　offset　of　points　on　shape　simultaneously.　To　address　this　issue,　we　propose　a　decomposed　latent　diffusion　model　that　separately　captures　consistency　information　and　offset　information　in　the　latent　space　with　feature　decoupling.　To　learn　effective　consistency　information,　the　consistency　constraint　among　different　point　clouds　with　a　shape　is　imposed　in　the　latent　space.　Then,　based　on　the　decomposed　features,　we　further　design　a　geometry　diffusion　model.　We　predict　key　points　with　consistency　information　to　guide　the　diffusion　model.　Therefore,　the　diffusion　model　can　achieve　comprehensive　and　strong　geometry　feature　extraction.　Experiments　show　that　our　method　achieved　state-of-the-art　generation　performance　on　the　ShapeNet　dataset.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.