Camera-based　stereo　3D　object　detection　estimates　3D　properties　of　objects　with　binocular　images　only,　which　is　a　cost-effective　solution　for　autonomous　driving.　The　state-of-the-art　methods　mainly　improve　the　detection　accuracy　of　general　objects　by　designing　ingenious　stereo　matching　algorithms　or　complex　pipeline　modules.　Moreover,　additional　fine-grained　annotations,　such　as　masks　or　LiDAR　point　clouds,　are　often　introduced　to　deal　with　the　occlusion　problems,　which　brings　in　high　manual　costs　for　this　task.　To　address　the　detection　bottleneck　caused　by　occlusion　in　a　more　cost-effective　manner,　we　develop　a　novel　stereo　3D　object　detection　method　named　DSC3D,　which　achieves　significant　improvements　for　occluded　objects　without　introducing　additional　supervision.　Specifically,　we　first　report　the　ambiguity　in　feature　sampling,　which　refers　to　the　presence　of　noisy　features　in　the　sampling　for　occluded　objects.　Then,　we　propose　the　Epipolar　Constraint　Deform-Attention　(ECDA)　module　to　address　the　unreliable　left-right　correspondence　computation　in　stereo　matching　caused　by　occlusion,　which　reweights　epipolar　features　by　adaptively　aggregating　local　neighbor　information.　Furthermore,　to　ensure　that　3D　property　estimation　is　based　on　robust　object　features,　we　propose　visible　regions　guided　constraint　to　explicitly　guide　the　offset　learning　for　feature　sampling.　Extensive　experiments　conducted　on　the　KITTI　benchmark　have　demonstrated　the　proposed　DSC3D　outperforms　the　state-of-the-art　camera-based　methods.