  In this paper we propose an approach for monocular 3D object detection from a
single RGB image, which leverages a novel disentangling transformation for 2D
and 3D detection losses and a novel, self-supervised confidence score for 3D
bounding boxes. Our proposed loss disentanglement has the twofold advantage of
simplifying the training dynamics in the presence of losses with complex
interactions of parameters, and sidestepping the issue of balancing independent
regression terms. Our solution overcomes these issues by isolating the
contribution made by groups of parameters to a given loss, without changing its
nature. We further apply loss disentanglement to another novel, signed
Intersection-over-Union criterion-driven loss for improving 2D detection
results. Besides our methodological innovations, we critically review the AP
metric used in KITTI3D, which emerged as the most important dataset for
comparing 3D detection results. We identify and resolve a flaw in the 11-point
interpolated AP metric, affecting all previously published detection results
and particularly biases the results of monocular 3D detection. We provide
extensive experimental evaluations and ablation studies on the KITTI3D and
nuScenes datasets, setting new state-of-the-art results on object category car
by large margins.
