PointAugment is a an auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network. Different from existing auto-augmentation methods for 2D images, PointAugment is sample-aware and takes an adversarial learning strategy to jointly optimize an augmentor network and a classifier network, such that the augmentor can learn to produce augmented samples that best fit the classifier.
Source: PointAugment: an Auto-Augmentation Framework for Point Cloud ClassificationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Classification | 1 | 25.00% |
General Classification | 1 | 25.00% |
Point Cloud Classification | 1 | 25.00% |
Retrieval | 1 | 25.00% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |