  This paper focuses on monocular 3D object detection, one of the essential
modules in autonomous driving systems. A key challenge is that the depth
recovery problem is ill-posed in monocular data. In this work, we first conduct
a thorough analysis to reveal how existing methods fail to robustly estimate
depth when different geometry shifts occur. In particular, through a series of
image-based and instance-based manipulations for current detectors, we
illustrate existing detectors are vulnerable in capturing the consistent
relationships between depth and both object apparent sizes and positions. To
alleviate this issue and improve the robustness of detectors, we convert the
aforementioned manipulations into four corresponding 3D-aware data augmentation
techniques. At the image-level, we randomly manipulate the camera system,
including its focal length, receptive field and location, to generate new
training images with geometric shifts. At the instance level, we crop the
foreground objects and randomly paste them to other scenes to generate new
training instances. All the proposed augmentation techniques share the virtue
that geometry relationships in objects are preserved while their geometry is
manipulated. In light of the proposed data augmentation methods, not only the
instability of depth recovery is effectively alleviated, but also the final 3D
detection performance is significantly improved. This leads to superior
improvements on the KITTI and nuScenes monocular 3D detection benchmarks with
state-of-the-art results.
