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Localizing objects in 3D space and understanding their associated 3Dproperties is challenging given only monocular RGB images. The situation iscompounded by the loss of depth information during perspective projection. Wepresent Center3D, a one-stage anchor-free approach, to efficiently estimate 3Dlocation and depth using only monocular RGB images. By exploiting thedifference between 2D and 3D centers, we are able to estimate depthconsistently. Center3D uses a combination of classification and regression tounderstand the hidden depth information more robustly than each method alone.Our method employs two joint approaches: (1) LID: a classification-dominatedapproach with sequential Linear Increasing Discretization. (2) DepJoint: aregression-dominated approach with multiple Eigen's transformations for depthestimation. Evaluating on KITTI dataset for moderate objects, Center3D improvedthe AP in BEV from $29.7\%$ to $42.8\%$, and the AP in 3D from $18.6\%$ to$39.1\%$. Compared with state-of-the-art detectors, Center3D has achieved thebest speed-accuracy trade-off in realtime monocular object detection.

Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding