The abilityofarobottointeractwithitssurroundingsgreatlybenefitsfromasemanticallyrichscene graph.However,thisrequiresthescenegraphtohavesemanticinformationabouttheobjectsas wellastheirfunctionalinteractiveelements,alsoknownasparts.ConceptGraphs,astate-of-the-art piece ofresearch,fulfillsthefirstrequirementbutlacksthefine-grainedsegmentationofobject partsneededtofulfillthesecondrequirement.Inthisthesis,weintroduceanovelextensiontothe implementation ofConceptGraphs.WeproposetointegrateMask3DwithConceptGraphs.Wetrain the Mask3DmodelonadatasetresultingfromthemergerofARKitLabelMakerandSceneFun3D datasets. Thismodel,capableofpart-objectsegmentation,willenhanceConceptGraph’sabilityto capture fine-grainedinformationaboutobjectparts.Wealsotesthowwelloursystemworksby doing thetasksoffunctionalitysegmentationandtask-drivenaffordancegroundingthataredefined in SceneFun3D.Wepushforwardthestate-of-the-artperformanceontheSceneFun3Ddataset with ourimplementedsystem,demonstratingtheefficacyofourdevelopedsystem.Theresults obtained showanincreaseof6-9%inAveragePrecision(AP)atmeanIntersectionoverUnion (mIoU) thresholdsof25%and50%andover12%increaseinAP,theaverageoverdifferentIoU thresholds from0.5to0.95withastepof0.05.