Estimating　the　three-dimensional　(3D)　motion　from　sparse　laser　point　clouds　is　a　highly　challenging　endeavour　facing　computer　and　robotic　vision　engineers.　In　this　study,　a　novel　method　is　proposed　for　robustly　estimating　the　scene　flow　from　a　laser　scanner　assisted　by　a　camera.　Conditional　random　field　(CRF)　is　constructed　by　a　spatial　structure　of　point　clouds,　the　energy　of　which　is　minimised　by　a　synchronous　calibrated　image.　With　the　high　frame　rate　of　a　laser　scanner,　the　authors＇　method　allows　for　estimating　the　potential　motion　field　as　the　CRF　label.　The　authors　ran　an　experiment　on　a　public　dataset　to　demonstrate　that　their　method　can　accurately　estimate　rigid　motion　in　outdoor　scenes.　They　also　tested　the　method　on　a　laser　scanner　and　omni-directional　camera　system　to　find　that　it　also　accurately　estimates　the　rigid　and　semi-rigid　motion　of　objects　in　a　controlled　indoor　environment.