A　depth　map　can　be　used　in　many　applications　such　as　robotic　navigation,　driverless,　video　production　and　3D　reconstruction.　Both　passive　stereo　and　time-of-flight　(ToF)　cameras　can　provide　the　depth　map　for　the　captured　real　scenes,　but　they　both　have　innate　limitations.　Since　ToF　cameras　and　passive　stereo　are　intrinsically　complementary　for　certain　scenes,　it　is　desirable　to　appropriately　leverage　all　the　available　information　by　ToF　cameras　and　passive　stereo.　As　a　result,　this　study　proposes　an　approach　to　integrate　ToF　cameras　and　passive　stereo　to　obtain　high-accuracy　depth　maps.　The　main　contributions　are:　the　first　step　is　to　design　an　energy　cost　function　to　utilise　the　depth　map　from　ToF　cameras　to　guide　the　stereo　matching　of　passive　stereo　and　the　second　step　is　to　design　their　weight　function　for　depth　maps　pixel-level　fusion.　The　experiments　show　that　the　proposed　approach　achieves　the　improved　results　with　high　accuracy　and　robustness.