View　synthesis　optimization　(VSO)　is　one　of　the　core　techniques　for　depth　map　coding　in　three　dimensional　high　efficiency　video　coding　(3D-HEVC).　It　improves　the　quality　for　synthesized　views,　while　it　also　introduces　heavy　computational　complexity　caused　by　the　calculation　of　synthesized　view　distortion　change　(SVDC)　in　practice.　To　reduce　the　complexity,　this　paper　proposes　a　convolutional　neural　network-based　VSO　scheme　in　3D-HEVC.　First,　the　potential　factors　that　may　relate　to　the　encoding　complexity　are　explored.　Then,　based　on　this,　a　convolutional　neural　network　(CNN)　is　embedded　into　the　3D-HEVC　reference　software　HTM16.0　to　predict　the　depth　of　coding　units　(CUs).　The　complexity　of　SVDC　can　be　drastically　reduced　by　avoiding　the　brute-force　search　for　VSO　in　depth　0　and　depth　1.　Finally,　for　depth　2　and　depth　3,　the　zero　distortion　area　(ZDA)　is　determined　based　on　texture　smoothness　and　the　SVDC　calculation　for　that　area　is　skipped.　The　experimental　results　show　that　the　proposed　scheme　can　reduce　76.7%　encoding　time　without　any　significant　loss　for　the　3D　video　quality.　©　2020,　Springer　Nature　Switzerland　AG.