Point　cloud　upsampling　concerns　producing　a　dense　and　uniform　point　set　from　a　sparse　and　irregular　one.　Current　upsampling　methods　primarily　encounter　two　challenges:　(i)　insufficient　uni-modal　representations　of　sparse　point　clouds,　and　(ii)　inaccurate　estimation　of　geometric　details　in　dense　point　clouds,　resulting　in　suboptimal　upsampling　results.　To　tackle　these　challenges,　we　propose　MVP-Net,　a　multi-view　depth　image　guided　cross-modal　detail　estimation　distillation　network　for　point　cloud　upsampling,　in　which　the　multi-view　depth　images　of　point　clouds　are　fully　explored　to　guide　upsampling.　Firstly,　we　propose　a　cross-modal　feature　extraction　module,　consisting　of　two　branches　designed　to　extract　point　features　and　depth　image　features　separately.　This　setup　aims　to　produce　sufficient　cross-modal　representations　of　sparse　point　clouds.　Subsequently,　we　design　a　Multi-View　Depth　Image　to　Point　Feature　Fusion　(MVP)　block　to　fuse　the　cross-modal　features　in　a　fine-grained　and　hierarchical　manner.　The　MVP　block　is　incorporated　into　the　feature　extraction　module.　Finally,　we　introduce　a　paradigm　for　multi-view　depth　image-guided　detail　estimation　and　distillation.　The　teacher　network　fully　utilizes　paired　multi-view　depth　images　of　sparse　point　clouds　and　their　dense　counterparts　to　formulate　multi-hierarchical　representations　of　geometric　details,　thereby　achieving　high-fidelity　reconstruction.　Meanwhile,　the　student　network　takes　only　sparse　point　clouds　and　their　multi-view　depth　images　as　input,　and　it　learns　to　predict　the　multi-hierarchical　detail　representations　distilled　from　the　teacher　network.　Extensive　qualitative　and　quantitative　results　on　both　synthetic　and　real-world　datasets　demonstrate　that　our　method　outperforms　state-of-the-art　point　cloud　upsampling　methods.　©　2024　ACM.