Point Cloud Augmentation
PointAugment
Introduced by Li et al. in PointAugment: an Auto-Augmentation Framework for Point Cloud ClassificationPointAugment 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 Classification
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Classification | 1 | 25.00% |
| General Classification | 1 | 25.00% |
| Point Cloud Classification | 1 | 25.00% |
| Retrieval | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
