This　paper　presents　an　effective　3D　digitalization　technique　to　reconstruct　an　accurate　and　reliable　3D　environment　model　from　multi-view　stereo　for　an　environment-learning　mobile　robot.　The　novelty　of　this　paper　lies　in　the　introduction　of　nonrigid　motion　analysis　to　stereo　reconstruction　routine.　In　our　proposed　scheme,　reconstruction　task　is　decoupled　into　two　stages.　Firstly,　range　depth　of　feature　points　is　recovered　and　in　turn　is　used　for　building　a　polygonal　mesh　and　secondly,　projection　feedback　on　comparison　views,　which　is　generated　on　assumption　of　the　established　coarse　mesh　model,　is　carefully　introduced　to　deform　the　primitive　mesh　model　so　as　to　improve　its　quality　dramatically.　The　discrepancy　of　observation　on　comparison　views　and　the　corresponding　predictive　feedback　is　quantitatively　evaluated　by　optical　flow　field　and　is　employed　to　derive　the　corresponding　scene　flow　vector　field　subsequently,　which　is　then　used　for　surface　deformation.　As　optical　flow　vector　field　estimation　outperforms　traditional　dense　disparity　for　its　inherent　advantage　of　being　robust　to　illumination　change　and　being　optimized　and　smoothed　in　global　sense,　the　deformed　surface　can　be　improved　in　accuracy,　which　is　validated　by　experimental　results.　(c)　2012　Taylor　&　Francis　and　The　Robotics　Society　of　Japan