Geometric　primitives　contained　in　three-dimensional　(3D)　point　clouds　can　provide　the　meaningful　and　concise　abstraction　of　3D　data,　which　plays　a　vital　role　in　improving　3D　vision-based　intelligent　applications.　However,　how　to　efficiently　and　robustly　extract　multiple　geometric　primitives　from　point　clouds　is　still　a　challenge,　especially　when　multiple　instances　of　multiple　classes　of　geometric　primitives　are　present.　In　this　study,　a　novel　energy　minimisation-based　algorithm　for　multi-class　multi-instance　geometric　primitives　extraction　from　the　3D　point　cloud　is　proposed.　First,　an　improved　sampling　strategy　is　proposed　to　generate　model　hypotheses.　Then,　an　improved　strategy　to　establish　the　neighbourhood　is　proposed　to　help　construct　and　optimise　an　energy　function　for　points　labelling.　After　that,　hypotheses　and　parameters　of　models　are　refined.　Iterate　this　process　until　the　energy　does　not　decrease.　Finally,　models　of　multi-class　multi-instance　geometric　primitives　are　simultaneously　and　robustly　extracted　from　the　3D　point　cloud.　In　comparison　with　the　state-of-the-art　methods,　it　can　automatically　determine　the　classes　and　numbers　of　geometric　primitives　in　the　3D　point　cloud.　Experimental　results　with　synthetic　and　real　data　validate　the　proposed　algorithm.