Mobile　devices　usually　mount　a　depth　sensor　to　resolve　ill-posed　problems,　like　salient　object　detection　on　cluttered　background.　The　main　barrier　of　exploring　RGBD　data　is　to　handle　the　information　from　two　different　modalities.　To　cope　with　this　problem,　in　this　paper,　we　propose　a　boundary-aware　cross-modal　fusion　network　for　RGBD　salient　object　detection.　In　particular,　to　enhance　the　fusion　of　color　and　depth　features,　we　present　a　cross-modal　feature　sampling　module　to　balance　the　contribution　of　the　RGB　and　depth　features　based　on　the　statistics　of　their　channel　values.　In　addition,　in　our　multi-scale　dense　fusion　network　architecture,　we　not　only　incorporate　edge-sensitive　losses　to　preserve　the　boundary　of　the　detected　salient　region,　but　also　refine　its　structure　by　merging　the　estimated　saliency　maps　of　different　scales.　We　accomplish　the　multi-scale　saliency　map　merging　using　two　alternative　methods　which　produce　refined　saliency　maps　via　per-pixel　weighted　combination　and　an　encoder-decoder　network.　Extensive　experimental　evaluations　demonstrate　that　our　proposed　framework　can　achieve　the　state-of-the-art　performance　on　several　public　RGBD-based　datasets.