The　key　of　robots　operating　autonomously　in　dynamic　environments　is　understanding　the　dynamic　characteristics　of　objects.　This　paper　aims　to　detect　dynamic　objects　and　reconstruct　3D　static　maps　from　consecutive　scans　of　scenes.　Our　work　starts　from　an　encode-decode　network,　which　receives　two　range　maps　provided　by　a　Velodyne　HDL-64　laser　scanner　and　outputs　dynamic　probability　of　each　point.　Since　the　soft　segmentation　produced　by　the　network　tends　to　be　smooth,　a　3D　fully　connected　CRF　(Conditional　Random　Field)　is　proposed　to　improve　the　segmentation　performance.　Experiments　on　both　the　public　datasets　and　real-word　platform　demonstrate　the　effectiveness　of　our　method.