LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
- URL: http://arxiv.org/abs/2210.13488v1
- Date: Mon, 24 Oct 2022 18:00:04 GMT
- Title: LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
- Authors: Zhaoqi Leng, Guowang Li, Chenxi Liu, Ekin Dogus Cubuk, Pei Sun, Tong
He, Dragomir Anguelov, Mingxing Tan
- Abstract summary: LidarAugment is a search-based data augmentation strategy for 3D object detection.
We show LidarAugment can be customized for different model architectures.
It consistently improves convolution-based UPillars/StarNet/RSN and transformer-based SWFormer.
- Score: 55.45435708426761
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Data augmentations are important in training high-performance 3D object
detectors for point clouds. Despite recent efforts on designing new data
augmentations, perhaps surprisingly, most state-of-the-art 3D detectors only
use a few simple data augmentations. In particular, different from 2D image
data augmentations, 3D data augmentations need to account for different
representations of input data and require being customized for different
models, which introduces significant overhead. In this paper, we resort to a
search-based approach, and propose LidarAugment, a practical and effective data
augmentation strategy for 3D object detection. Unlike previous approaches where
all augmentation policies are tuned in an exponentially large search space, we
propose to factorize and align the search space of each data augmentation,
which cuts down the 20+ hyperparameters to 2, and significantly reduces the
search complexity. We show LidarAugment can be customized for different model
architectures with different input representations by a simple 2D grid search,
and consistently improve both convolution-based UPillars/StarNet/RSN and
transformer-based SWFormer. Furthermore, LidarAugment mitigates overfitting and
allows us to scale up 3D detectors to much larger capacity. In particular, by
combining with latest 3D detectors, our LidarAugment achieves a new
state-of-the-art 74.8 mAPH L2 on Waymo Open Dataset.
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