3D Point Cloud Data Augmentation

5 papers with code • 3 benchmarks • 3 datasets

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Most implemented papers

Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions

jiachens/ModelNet40-C 28 Jan 2022

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 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 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

RosettaWYzhang/AdaPC 11 Dec 2021

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 13 Oct 2022

Mixup is a simple and widely-used data augmentation technique that has proven effective in alleviating the problems of overfitting and data scarcity.