# Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding Source: [Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:18:12.808Z Authors: Yunlei Tang, Sebastian Dorn, Chiragkumar Savani Published: 2020-05-27T15:29:09.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()