At　present,　3D　reconstruction　from　images　has　made　notable　advancements　in　simple,　small-scale　scenes,　but　faces　significant　challenges　in　intricate,　expansive　architectural　scenes.　Focusing　on　the　early　stage　of　design　stage,　we　present　Drag2Build,　a　tool　for　converting　images　into　point　clouds　for　3D　reconstruction　and　modification　in　detailed　architectural　contexts.　Our　first　step　involved　the　creation　of　ArchiNet,　a　specialized　3D　reconstruction　dataset　dedicated　to　elaborate　architectural　scenes.　Next,　we　developed　a　3D　reconstruction　approach　using　a　conditional　denoising　diffusion　model,　enhanced　by　incorporating　a　model　for　segmenting　objects,　thereby　improving　segmentation　and　identification　in　complex　scenes.　Additionally,　our　system　features　an　interactive　component　that　allows　for　immediate　modification　of　2D　images　via　an　easy　drag-and-drop　action,　synchronously　updating　3D　architectural　point　clouds.　The　performance　of　Drag2Build　in　3D　reconstruction　precision　was　assessed　and　benchmarked　against　mainstream　methods　using　ArchiNet.　The　experiments　showed　that　our　approach　is　capable　of　producing　high-quality　3D　point　clouds,　facilitating　swift　editing　and　efficient　handling　of　intricate　backgrounds.　©　2024　and　published　by　the　Association　for　Computer-Aided　Architectural　Design　Research　in　Asia　(CAADRIA),　Hong　Kong.