In　this　paper,　a　new　method　called　the　Virtual　Cutting　Method　is　proposed　to　evaluate　the　angularity　index　(AI)　values　of　3D　point　cloud　coarse　aggregate　images　with　the　aim　of　characterizing　the　angularity　of　aggregates　on　conveyor　belts.　The　3D　point　cloud　images　of　coarse　aggregates　were　first　captured,　preprocessed,　and　segmented　into　single　3D　aggregate　objects.　Based　on　the　processed　3D　aggregate　images,　intersection　contours　were　extracted　using　a　series　of　intersection　planes　with　an　equivalent　angle　between　two　adjacent　planes.　The　AI　was　evaluated　by　averaging　the　angularity　of　the　contours　using　the　gradient　method,　which　was　used　in　the　AIMS2　system.　Statistical　analysis　was　then　performed　to　select　the　optimum　angle　between　two　adjacent　planes.　It　was　found　that　an　angle　of　five　degrees　was　the　ideal　angle,　as　it　can　balance　the　execution　time　and　effectiveness　of　the　method.　Finally,　the　AI　results　of　the　Virtual　Cutting　Method　were　compared　with　those　of　2D　and　3D　Projection　Methods.　It　was　found　that　the　AI　rankings　of　the　three　methods　for　different　aggregate　textures　are　generally　consistent.　The　findings　of　this　study　conclude　that　the　Virtual　Cutting　Method　can　be　employed　to　quantify　the　angularity　of　a　single　aggregate　or　aggregates　in　piles　on　conveyor　belts　based　on　3D　point　cloud　images.