Learning-based　Multiple-View　Stereo　(MVS)　aims　to　reconstruct　dense　3D　scene　representation.　However,　previous　methods　utilize　additional　2D　network　modules　to　learn　the　cross　view　visibility　for　cost　aggregation,　ignoring　the　consistency　assumption　of　2D　contextual　features　in　the　3D　depth　direction.　In　addition,　the　current　multi-stage　depth　inference　model　still　requires　a　high　depth　sampling　rate,　and　depth　hypothesis　is　sampled　within　static　and　preset　depth　range,　which　is　prone　to　generate　errorneous　depth　inference　in　the　object　boundary　and　occluded　area.　To　alleviate　these　problems,　a　multi-view　stereo　network　based　on　edge　assisted　epipolar　Transformer　is　proposed.　The　improvements　of　this　work　over　the　state　of　the　art　are　as:　Depth　regression　is　replaced　by　the　multi-depth　hypotheses　classification　to　ensure　the　accuracy　with　limited　depth　sampling　rate　and　GPU　consumption.　Epipolar　Transformer　block　is　developed　for　reliable　cross　view　cost　aggregation,　and　edge　detection　branch　is　designed　to　constrain　the　consistency　of　edge　features　in　the　epipolar　direction.　A　dynamic　depth　range　sampling　mechanism　based　on　probabilistic　cost　volume　is　applied　to　improve　the　accuracy　of　uncertain　areas.　Comprehensive　comparisons　with　the　state　of　the　art　are　conducted　on　public　benchmarks,　which　indicate　that　the　proposed　method　can　reconstruct　dense　scene　representations　with　limited　memory　bottleblock.　Specifically,　compared　with　Cas-MVSNet,　the　memory　consumption　is　reducted　by　35%,　the　depth　sampling　rate　is　reduced　by　about　50%,　and　the　overall　error　on　DTU　datasets　is　reduced　from　0.355　to　0.325.　©　2023　Science　Press.　All　rights　reserved.