Particle　shapes　significantly　affect　viscosity　and　flow　behavior　of　energetic　materials,　and　therefore　affect　their　packability　and　processability.　This　study　presents　a　computational　geometry　framework　for　automatically　quantifying　two-dimensional　(2D)　and　three-dimensional　(3D)　particle　shapes　of　energetic　materials.　A　specimen　by　mixing　three　typical　energetic　materials　including　HMX　(octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine),　RDX　(1,3,5-Trinitroperhydro-1,3,5-triazine)　and　AP　(Ammonium　Perchlorate)　particles　is　used　in　this　study.　This　specimen　is　scanned　by　high-resolution　X-ray　computed　tomography　(X-ray　CT),　yielding　a　volumetric　image.　An　improved　watershed　analysis　algorithm　is　used　to　process　the　volumetric　image　to　identify　individual　3D　particles.　The　stereology　sampling　method　is　used　to　obtain　2D　projections　of　3D　particles.　Computational　geometry　techniques　are　developed　by　this　study　to　analyze　2D　particle　projections　and　3D　particle　geometries　to　compute　seven　commonly　used　shape　descriptors,　including　convexity,　circularity,　intercept　sphericity,　area　sphericity,　diameter　sphericity,　circle　ratio　sphericity,　and　surface　area　sphericity.　Results　show　that　those　different　shape　descriptors　of　energetic　materials　can　be　divided　into　three　groups　based　on　their　numerical　ranges.　This　study　also　evaluates　the　effectiveness　and　accuracy　of　2D　shape　descriptors　for　quantifying　the　true　3D　shapes.　The　inconsistent　characterization　results　between　2D　and　3D　shape　descriptors　suggest　that　researchers　should　be　cautious　when　using　2D　images　to　characterize　3D　particle　shapes　of　energetic　materials.　The　computational　geometry　framework　and　particle　shape　analysis　results　presented　in　this　study　can　be　potentially　useful　in　numerical　modeling,　experimental　analysis,　and　theoretical　investigation　for　energetic　materials.　©　2022,　The　Author(s).