PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies (2206.04670v2)

Abstract: PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks such as PointMLP and Point Transformer, we find that a large portion of the performance gain is due to improved training strategies, i.e. data augmentation and optimization techniques, and increased model sizes rather than architectural innovations. Thus, the full potential of PointNet++ has yet to be explored. In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions. First, we propose a set of improved training strategies that significantly improve PointNet++ performance. For example, we show that, without any change in architecture, the overall accuracy (OA) of PointNet++ on ScanObjectNN object classification can be raised from 77.9% to 86.1%, even outperforming state-of-the-art PointMLP. Second, we introduce an inverted residual bottleneck design and separable MLPs into PointNet++ to enable efficient and effective model scaling and propose PointNeXt, the next version of PointNets. PointNeXt can be flexibly scaled up and outperforms state-of-the-art methods on both 3D classification and segmentation tasks. For classification, PointNeXt reaches an overall accuracy of 87.7 on ScanObjectNN, surpassing PointMLP by 2.3%, while being 10x faster in inference. For semantic segmentation, PointNeXt establishes a new state-of-the-art performance with 74.9% mean IoU on S3DIS (6-fold cross-validation), being superior to the recent Point Transformer. The code and models are available at https://github.com/guochengqian/pointnext.

Overview of "PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies"

The paper "PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies" presents a comprehensive paper aimed at bolstering the widely adopted PointNet++ architecture for point cloud processing. The paper elucidates the untapped potential of PointNet++ by integrating advanced training techniques and model scaling strategies, culminating in the enhanced architecture named PointNeXt.

Key Contributions

1. Enhanced Training Strategies:
   • The authors identify that recent advancements in training methodologies, such as improved data augmentation and optimization techniques, have largely contributed to performance gains observed in newer models.
   • By systematically adopting these modern training strategies, the authors markedly improved PointNet++'s performance, achieving an overall accuracy increase on tasks like ScanObjectNN by 8.2% without architectural changes.
2. PointNeXt Design:
   • Inverted residual bottleneck design and separable MLPs are introduced into PointNet++ to improve model scalability and efficiency.
   • PointNeXt exhibits flexibility in scaling and outperforms state-of-the-art models in both classification and segmentation tasks. For instance, it achieves 87.7% accuracy on ScanObjectNN, surpassing PointMLP by 2.3%.
3. Empirical Findings:
   • Comprehensive evaluations demonstrate that a significant portion of the performance increase in contemporary methods over PointNet++ is attributed to enhanced training and not solely architectural advances.
   • For instance, training modifications alone resulted in a 13.6% increase in mean IoU on the S3DIS benchmark, outperforming many modern architectures.
4. Efficient Scaling:
   • PointNeXt is scalable, achieving superior performance across various benchmarks while maintaining computational efficiency, boasting faster inference speeds and lower FLOPs compared to other leading methods.

Implications and Future Prospects

The work presented in this paper has far-reaching implications in the domain of 3D point cloud processing:

• Practical Applications: The findings advocate for leveraging improved training mechanisms to enhance existing architectures, potentially leading to more data-efficient and computation-efficient models in practical applications such as autonomous driving, robotics, and augmented reality.
• Theoretical Implications: The paper challenges the prevalent trend of focusing predominantly on architectural innovations by highlighting the substantial impact of training strategies. This may encourage a shift towards more holistic model development approaches.
• Potential for Further Research: The insights gained from this work provide pathways for further exploration in model scaling techniques and optimization strategies, particularly in the context of large-scale 3D datasets.

In conclusion, the paper effectively revitalizes PointNet++ by demonstrating that established architectures can achieve state-of-the-art performance with thoughtful integration of modern training strategies. PointNeXt stands as a compelling alternative for researchers and practitioners in need of efficient and powerful point cloud processing solutions.