SurfaceAug: Closing the Gap in Multimodal Ground Truth Sampling

Autor:	Rubel, Ryan, Clark, Nathan, Dudash, Andrew
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Contains eight pages and three figures. A version of this document was submitted to CVPR 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.03808 Zobrazit plný text záznamu View this record from Arxiv