  It is counter-intuitive that multi-modality methods based on point cloud and
images perform only marginally better or sometimes worse than approaches that
solely use point cloud. This paper investigates the reason behind this
phenomenon. Due to the fact that multi-modality data augmentation must maintain
consistency between point cloud and images, recent methods in this field
typically use relatively insufficient data augmentation. This shortage makes
their performance under expectation. Therefore, we contribute a pipeline, named
transformation flow, to bridge the gap between single and multi-modality data
augmentation with transformation reversing and replaying. In addition,
considering occlusions, a point in different modalities may be occupied by
different objects, making augmentations such as cut and paste non-trivial for
multi-modality detection. We further present Multi-mOdality Cut and pAste
(MoCa), which simultaneously considers occlusion and physical plausibility to
maintain the multi-modality consistency. Without using ensemble of detectors,
our multi-modality detector achieves new state-of-the-art performance on
nuScenes dataset and competitive performance on KITTI 3D benchmark. Our method
also wins the best PKL award in the 3rd nuScenes detection challenge. Code and
models will be released at https://github.com/open-mmlab/mmdetection3d.
