  Despite recent advances in both model architectures and data augmentation,
multimodal object detectors still barely outperform their LiDAR-only
counterparts. This shortcoming has been attributed to a lack of sufficiently
powerful multimodal data augmentation. To address this, we present SurfaceAug,
a novel ground truth sampling algorithm. SurfaceAug pastes objects by
resampling both images and point clouds, enabling object-level transformations
in both modalities. We evaluate our algorithm by training a multimodal detector
on KITTI and compare its performance to previous works. We show experimentally
that SurfaceAug outperforms existing methods on car detection tasks and
establishes a new state of the art for multimodal ground truth sampling.
