3D Point Cloud Data Augmentation
5 papers with code • 3 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in 3D Point Cloud Data Augmentation
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ModelNet
ScanObjectNN
ModelNet40-C
Most implemented papers
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications.
PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
liruihui/PointAugment
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CVPR 2020
We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network.
PointMixup: Augmentation for Point Clouds
yunlu-chen/PointMixup
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ECCV 2020
In this paper, we define data augmentation between point clouds as a shortest path linear interpolation.
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.
SageMix: Saliency-Guided Mixup for Point Clouds
mlvlab/SageMix
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Mixup is a simple and widely-used data augmentation technique that has proven effective in alleviating the problems of overfitting and data scarcity.
