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arXiv · 2312.03808

SurfaceAug: Closing the Gap in Multimodal Ground Truth Sampling

Ryan Rubel·Nathan Clark·Andrew Dudash
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Abstract

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.

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Ryan Rubel, Nathan Clark, Andrew Dudash. 2023-12-06. SurfaceAug: Closing the Gap in Multimodal Ground Truth Sampling. https://arxiv.org/abs/2312.03808

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