  Data augmentation is a key component of CNN based image recognition tasks
like object detection. However, it is relatively less explored for 3D object
detection. Many standard 2D object detection data augmentation techniques do
not extend to 3D box. Extension of these data augmentations for 3D object
detection requires adaptation of the 3D geometry of the input scene and
synthesis of new viewpoints. This requires accurate depth information of the
scene which may not be always available. In this paper, we evaluate existing 2D
data augmentations and propose two novel augmentations for monocular 3D
detection without a requirement for novel view synthesis. We evaluate these
augmentations on the RTM3D detection model firstly due to the shorter training
times . We obtain a consistent improvement by 4% in the 3D AP (@IoU=0.7) for
cars, ~1.8% scores 3D AP (@IoU=0.25) for pedestrians & cyclists, over the
baseline on KITTI car detection dataset. We also demonstrate a rigorous
evaluation of the mAP scores by re-weighting them to take into account the
class imbalance in the KITTI validation dataset.
