Three-dimensional　(3D)　reconstruction　of　shapes　is　an　important　research　topic　in　the　fields　of　computer　vision,　computer　graphics,　pattern　recognition,　and　virtual　reality.　Existing　3D　reconstruction　methods　usually　suffer　from　two　bottlenecks:　(1)　they　involve　multiple　manually　designed　states　which　can　lead　to　cumulative　errors,　but　can　hardly　learn　semantic　features　of　3D　shapes　automatically;　(2)　they　depend　heavily　on　the　content　and　quality　of　images,　as　well　as　precisely　calibrated　cameras.　As　a　result,　it　is　difficult　to　improve　the　reconstruction　accuracy　of　those　methods.　3D　reconstruction　methods　based　on　deep　learning　overcome　both　of　these　bottlenecks　by　automatically　learning　semantic　features　of　3D　shapes　from　low-quality　images　using　deep　networks.　However,　while　these　methods　have　various　architectures,　in-depth　analysis　and　comparisons　of　them　are　unavailable　so　far.　We　present　a　comprehensive　survey　of　3D　reconstruction　methods　based　on　deep　learning.　First,　based　on　different　deep　learning　model　architectures,　we　divide　3D　reconstruction　methods　based　on　deep　learning　into　four　types,　recurrent　neural　network,　deep　autoencoder,　generative　adversarial　network,　and　convolutional　neural　network　based　methods,　and　analyze　the　corresponding　methodologies　carefully.　Second,　we　investigate　four　representative　databases　that　are　commonly　used　by　the　above　methods　in　detail.　Third,　we　give　a　comprehensive　comparison　of　3D　reconstruction　methods　based　on　deep　learning,　which　consists　of　the　results　of　different　methods　with　respect　to　the　same　database,　the　results　of　each　method　with　respect　to　different　databases,　and　the　robustness　of　each　method　with　respect　to　the　number　of　views.　Finally,　we　discuss　future　development　of　3D　reconstruction　methods　based　on　deep　learning.