We　address　the　problem　of　localizing　waste　objects　from　a　color　image　and　an　optional　depth　image,　which　is　a　key　perception　component　for　robotic　interaction　with　such　objects.　Specifically,　our　method　integrates　the　intensity　and　depth　information　at　multiple　levels　of　spatial　granularity.　Firstly,　a　scene-level　deep　network　produces　an　initial　coarse　segmentation,　based　on　which　we　select　a　few　potential　object　regions　to　zoom　in　and　perform　fine　segmentation.　The　results　of　the　above　steps　are　further　integrated　into　a　densely　connected　conditional　random　field　that　learns　to　respect　the　appearance,　depth,　and　spatial　affinities　with　pixel-level　accuracy.　In　addition,　we　create　a　new　RGBD　waste　object　segmentation　dataset,　MJU-Waste,　that　is　made　public　to　facilitate　future　research　in　this　area.　The　efficacy　of　our　method　is　validated　on　both　MJU-Waste　and　the　Trash　Annotation　in　Context　(TACO)　dataset.