SurfaceAug: Closing the Gap in Multimodal Ground Truth Sampling
Ryan Rubel Nathan Clark Andrew Dudash
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.