  Localizing objects in 3D space and understanding their associated 3D
properties is challenging given only monocular RGB images. The situation is
compounded by the loss of depth information during perspective projection. We
present Center3D, a one-stage anchor-free approach, to efficiently estimate 3D
location and depth using only monocular RGB images. By exploiting the
difference between 2D and 3D centers, we are able to estimate depth
consistently. Center3D uses a combination of classification and regression to
understand the hidden depth information more robustly than each method alone.
Our method employs two joint approaches: (1) LID: a classification-dominated
approach with sequential Linear Increasing Discretization. (2) DepJoint: a
regression-dominated approach with multiple Eigen's transformations for depth
estimation. Evaluating on KITTI dataset for moderate objects, Center3D improved
the 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 the
best speed-accuracy trade-off in realtime monocular object detection.
