We　propose　an　innovative　method　of　material　classification　based　on　the　imaging　model　of　pulsed　time-of-flight　(ToF)　camera　integrated　with　the　unique　signature　that　describes　physical　properties　of　each　material　named　reflection　point　spread　function　(RPSF).　First,　the　optimization　method　reduces　the　effect　of　material　surface　interreflection,　which　would　affect　RPSF　and　lead　to　decreased　accuracy　in　classification,　by　alternating　direction　method　of　multipliers　(ADMM).　A　method　named　feature　vector　normalization　is　proposed　to　extract　material　RPSF　features.　Second,　according　to　the　nonlinearity　of　the　feature　vectors,　the　structure　of　hidden　layer　neurons　of　radial　basis　function　(RBF)　neural　network　is　optimized　based　on　singular　value　decomposition　(SVD)　to　improve　generalization.　Finally,　the　similar　appearance　of　plastics　and　metals　are　classified　on　turntable-based　measurement　system　by　own　design.　The　average　classification　accuracy　reaches　93.3%,　and　the　highest　classification　accuracy　reaches　94.6%.　©　2021,　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH,　DE　part　of　Springer　Nature.