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

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 and an extension of CenterNet, 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 [6] for depth estimation. Evaluating on KITTI dataset [8] for moderate objects, Center3D improved the AP in BEV from $29.7\%$ to $\mathbf {43.5\%}$, and the AP in 3D from $18.6\%$ to $\mathbf {40.5\%}$. Compared with state-of-the-art detectors, Center3D has achieved a better speed-accuracy trade-off in realtime monocular object detection.