Accurate　recovery　of　3D　geometrical　surfaces　from　calibrated　2D　multi-view　images　is　a　fundamental　yet　active　research　area　in　computer　vision.　Despite　the　steady　progress　in　multi-view　stereo　(MVS)　reconstruction,　many　existing　methods　are　still　limited　in　recovering　fine-scale　details　and　sharp　features　while　suppressing　noises,　and　may　fail　in　reconstructing　regions　with　less　textures.　To　address　these　limitations,　this　paper　presents　a　detail-preserving　and　content-aware　variational　(DCV)　MVS　method,　which　reconstructs　the　3D　surface　by　alternating　between　reprojection　error　minimization　and　mesh　denoising.　In　reprojection　error　minimization,　we　propose　a　novel　inter-image　similarity　measure,　which　is　effective　to　preserve　fine-scale　details　of　the　reconstructed　surface　and　builds　a　connection　between　guided　image　filtering　and　image　registration.　In　mesh　denoising,　we　propose　a　content-aware　l(p)-minimization　algorithm　by　adaptively　estimating　the　p　value　and　regularization　parameters.　Compared　with　conventional　isotropic　mesh　smoothing　approaches,　the　proposed　method　is　much　more　promising　in　suppressing　noise　while　preserving　sharp　features.　Experimental　results　on　benchmark　data　sets　demonstrate　that　our　DCV　method　is　capable　of　recovering　more　surface　details,　and　obtains　cleaner　and　more　accurate　reconstructions　than　the　state-of-the-art　methods.　In　particular,　our　method　achieves　the　best　results　among　all　published　methods　on　the　Middlebury　dino　ring　and　dino　sparse　data　sets　in　terms　of　both　completeness　and　accuracy.