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PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu
TL;DR
PointAugment addresses limited and insufficiently diverse 3D point-cloud training data, where conventional fixed random perturbations may inadequately train classifiers. It uses sample-aware, jointly optimized augmentation and classification to produce richer samples, helping classifiers learn intra-class-insensitive features while extending beyond classification requires task-specific designs.
Problem
3D point-cloud datasets have limited quantity, labels, and diversity, while conventional small fixed-range perturbations may provide insufficient training for classification networks.
Method
PointAugment jointly optimizes a sample-aware augmentor and classifier, using shape-wise transformations and point-wise displacements to generate augmented samples from each input.
Results
PointAugment helps classifiers process a wider variety of training samples, uncover intrinsic class variations, and discover intra-class-insensitive features.
Takeaways & Limitations
Classifier-guided, sample-specific augmentation provides a framework for enriching point-cloud training diversity while adapting augmentation to individual samples.
Takeaways & Limitations
Extending PointAugment to other tasks requires task-specific designs, such as part-aware augmentation for part segmentation and richer instance-aware transformations for object detection.
Abstract
from arXiv · showhide
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. 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. Moreover, we formulate a learnable point augmentation function with a shape-wise transformation and a point-wise displacement, and carefully design loss functions to adopt the augmented samples based on the learning progress of the classifier. Extensive experiments also confirm PointAugment's effectiveness and robustness to improve the performance of various networks on shape classification and retrieval.
1. Introduction
Point-cloud datasets are small and insufficiently diverse, making overfitting and limited generalization persistent concerns. PointAugment addresses these limitations with sample-aware, jointly optimized augmentation that improves classification across datasets and networks.
- ModelNet40 contains only 12,311 models across 40 categories, illustrating the limited quantity and diversity of 3D training data.
- Data augmentation enlarges training-sample quantity and diversity to reduce overfitting and improve network generalization.
- Conventional point-cloud augmentation randomly applies small, fixed perturbations because the 3D augmentation space is large and labels must be preserved.
- Existing methods separate network training from augmentation and apply identical transformations to every sample, ignoring shape complexity.
- PointAugment learns sample-specific augmentation functions combining shape-wise transformation and point-wise displacement, while jointly optimizing augmentor and classifier networks.
- PointAugment produces clear classification improvements on ModelNet40 and SHREC16 across PointNet, PointNet++, RSCNN, and DGCNN, and is also evaluated for retrieval and robustness.
2. Related Work
Prior work explores image augmentation policies and adversarial strategies, while point-cloud augmentation remains largely handcrafted and fixed. PointAugment differs by learning sample-specific augmentation functions for 3D data.
- Image augmentation research includes generated samples, image combinations, GANs, and learned combinations of predefined transformations.
- AutoAugment, FastAugment, and PBA search for augmentation policies or hyperparameters, generally producing a fixed strategy for all training samples.
- Adversarial image-augmentation methods learn policies on target tasks by maximizing augmented-sample loss, whereas PointAugment uses an explicitly designed boundary to adjust difficulty.
- Point-cloud networks commonly use fixed random rotation, scaling, and jittering rules, with no prior auto-augmentation approach identified by the authors.
- Point-cloud research also studies local structures, graph convolutions, geometric elements, and projections into regular spaces for convolutional processing.
3. Overview
PointAugment consists of an augmentor and classifier trained together so classifier feedback guides sample-specific augmentation. Its design covers geometric transformations, point displacements, and dynamically increasing training difficulty.
- The framework contains two components—augmentor A and classifier C—and feeds each input sample through A before classifier training.
- The classifier processes original and augmented samples separately, then supplies feedback that guides augmentor training.
- PointAugment regresses a distinct augmentation function for each sample by considering its underlying geometric structure.
- The learned 3D augmentation combines shape-wise transformations such as rotation and scaling with point-wise displacement or jittering.
- End-to-end joint optimization makes augmented samples more challenging as the classifier becomes stronger, using losses that dynamically adjust augmentation difficulty.
4. Method
PointAugment jointly trains a sample-aware augmentor and classifier, generating shape- and point-level variations while controlling augmentation difficulty and fidelity. The framework alternates updates to both networks so augmented samples become more challenging without losing shape distinctiveness.
- Augmentor: PointAugment’s sample-aware augmentor generates a separate augmentation function for each input point cloud.The augmentor regresses a shape-wise transformation and point-wise displacement from the input sample.
- Augmentor: The augmented sample is computed as P · M + D, where M combines mainly rotation and scaling and D provides point-wise translation and jittering.
- Classifier: The classifier processes both original and augmented samples, predicts their class labels, and penalizes differences between their global features.The feature-consistency term is intended to stabilize training across the two inputs.
- Augmentor loss: The augmentor seeks samples with higher classification loss than the original while constraining the loss increase to preserve shape distinctiveness.The method treats the loss difference ξ = L(P′) − L(P) as augmentation magnitude and upperbounds the augmented loss by ρL(P).
- Augmentor loss: The target augmentation difficulty increases as the classifier improves, because the dynamic ρ and ξo grow with prediction probability.At the start of training, the classifier receives more attention and the target augmentation magnitude is kept small.
- Training: The end-to-end strategy alternates augmentor and classifier updates while holding the other network fixed, using the respective augmentor and classifier losses.The implementation trains with mixed original and previously augmented samples and uses dropout on the learned transformations and displacements.
5. Experiments
Experiments evaluate PointAugment across multiple datasets, classifiers, tasks, robustness settings, ablations, and augmentor designs. The results show consistent gains in classification and retrieval, while analyses identify how its components, losses, and training choices affect performance.
- Shape classification: PointAugment improves classification accuracy for every evaluated classifier across MN40, MN10, and SR16.On MN40, DGCNN+PointAugment reaches 93.4%; the method is especially effective on the imbalanced SR16 dataset.
- Shape retrieval: PointAugment improves shape retrieval for all four classifier networks, with gains exceeding 5% for PointNet and PointNet++.Visual retrieval results also show more accurate retrievals than PointNet with conventional augmentation.
- Ablation study: Point-wise displacement and shape-wise transformation each contribute to more effective augmented samples in the ablation study.The baseline PointNet++ accuracy is 90.7%, while Models B and C add displacement or transformation separately and Model D combines both.
- Ablation study: Dropout and mixed training samples both slightly improve the overall results when compared across the ablation models.These strategies are used to stabilize training and explore more transformations.
- Augmentor analysis: PointAugment initially trails the baseline because its augmentor is randomly initialized, then rapidly surpasses it as training continues.An unrestricted objective instead creates unstable training and worse classification performance, motivating the designed augmentor loss.
- Augmentor analysis: The loss weight λ balances classification focus and augmentation difficulty, with λ = 1.0 selected after smaller and larger values hinder performance.λ = 0.5 can produce negligible augmentation, whereas λ = 2.0 can make samples too difficult for the classifier.
6. Conclusion
PointAugment jointly optimizes sample-aware augmentation and classification for 3D point clouds, using shape-wise and point-wise operations with dynamically adjusted magnitudes. Experiments on MN40 and SR16 show improved performance across four representative networks.
- PointAugment jointly optimizes augmentor and classifier networks so augmentation improves from classifier feedback while the classifier learns from more varied samples.
- Its sample-aware augmentor produces input-specific functions using shape-wise transformation and point-wise displacement.
- A novel loss dynamically adjusts augmentation magnitude according to the classifier’s learning state across training stages.
- Experiments demonstrate performance improvements for four representative networks on the MN40 and SR16 datasets.