RGBD　cameras　that　capture　color　and　depth　information　have　a　wide　range　of　applications　such　as　robotics,　autonomous　driving,　and　cons　umer　electronics.　Traditional　RGBD　imaging　usually　requires　multi-cameras　or　extra　active　illumination　which　inevitably　leads　to　bulky　or　complex　imaging　systems,　hindering　the　growing　demand　for　compact　integrated　optical　devices.　Optical　metasurface　have　emerged　as　a　powerful　substitute　to　the　traditional　diffractive　optical　element　due　to　the　superior　dispersion　manipulation　and　extremely　compact　size.　However,　it　is　still　a　great　challenge　to　realize　a　single-shot　monocular　metasurface　camera,　due　to　the　strong　and　unignorable　wavelength-dependent　aberrations.　In　this　work,　we　demonstrate　an　end-to-end　compact　single-shot　monocular　metasurface　camera　for　RGBD　imaging.　By　utilizing　end-to-end　joint　optimization　framework　to　learn　metasurface　physical　structure　in　conjunction　with　deep　neural　networks　reconstruction　algorithm,　the　multidimensional　light　field　information　RGBD　of　a　scene　in　a　single　shot　can　be　reconstructed　within　a　depth　range　of　0.5　m.　Compared　with　traditional　lens-based　RGBD　imaging,　our　proposed　metasurface-based　imaging　achieves　an　improvement　of　about　2　dB　in　chromatic　imaging　and　a　4　times　increase　in　depth　estimation　accuracy.　Our　proposed　scheme　could　facilitate　the　further　development　of　the　intelligent　computational　meta-optics　in　diverse　fields　ranging　from　machine　vision　to　biomedical　imaging.　©　2024　Elsevier　B.V.