  Current geometry-based monocular 3D object detection models can efficiently
detect objects by leveraging perspective geometry, but their performance is
limited due to the absence of accurate depth information. Though this issue can
be alleviated in a depth-based model where a depth estimation module is plugged
to predict depth information before 3D box reasoning, the introduction of such
module dramatically reduces the detection speed. Instead of training a costly
depth estimator, we propose a rendering module to augment the training data by
synthesizing images with virtual-depths. The rendering module takes as input
the RGB image and its corresponding sparse depth image, outputs a variety of
photo-realistic synthetic images, from which the detection model can learn more
discriminative features to adapt to the depth changes of the objects. Besides,
we introduce an auxiliary module to improve the detection model by jointly
optimizing it through a depth estimation task. Both modules are working in the
training time and no extra computation will be introduced to the detection
model. Experiments show that by working with our proposed modules, a
geometry-based model can represent the leading accuracy on the KITTI 3D
detection benchmark.
