Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline (2106.05304v1)

Abstract: Processing point cloud data is an important component of many real-world systems. As such, a wide variety of point-based approaches have been proposed, reporting steady benchmark improvements over time. We study the key ingredients of this progress and uncover two critical results. First, we find that auxiliary factors like different evaluation schemes, data augmentation strategies, and loss functions, which are independent of the model architecture, make a large difference in performance. The differences are large enough that they obscure the effect of architecture. When these factors are controlled for, PointNet++, a relatively older network, performs competitively with recent methods. Second, a very simple projection-based method, which we refer to as SimpleView, performs surprisingly well. It achieves on par or better results than sophisticated state-of-the-art methods on ModelNet40 while being half the size of PointNet++. It also outperforms state-of-the-art methods on ScanObjectNN, a real-world point cloud benchmark, and demonstrates better cross-dataset generalization. Code is available at https://github.com/princeton-vl/SimpleView.

Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline

The paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline" undertakes a critical reassessment of methodologies employed in processing 3D point cloud data, a fundamental task in several domains such as autonomous driving, robotics, and scene understanding. The authors, affiliated with Princeton University, introduce a structured and comprehensive evaluation of past and present models by revisiting auxiliary factors unrelated to the network architecture itself, which influence classification performance significantly. This exploration leads to two pivotal findings that challenge prevailing assumptions in the community.

Key Findings and Contributions

1. Impact of Protocols Over Architectures: The paper underscores the substantial effect of external factors such as data augmentation strategies, evaluation schemes, and loss functions on the performance of classification models. These elements can obscure the actual contribution of the architectural design improvements. A notable revelation is that when these factors are aligned, PointNet++, an older architecture, displays performance comparable to more recent techniques.
2. Introduction of SimpleView: Beyond the assessment of existing architectures, the authors introduce a lightweight, projection-based approach termed SimpleView. Although less complex than contemporary state-of-the-art models, SimpleView outperforms these models on established benchmarks such as ModelNet40 and ScanObjectNN. This approach underscores the potential of simplicity and efficient feature extraction in achieving competitive results with fewer parameters and computational resources.

Experimental Approach and Results

The paper extensively evaluates different architectures using multiple protocols that vary in data augmentation, model selection criteria, loss functions, and ensemble schemes. This comprehensive analysis delineates the critical role these protocols play over architectural innovations. Notably, the adoption of the best performing combination of these elements with SimpleView elevates its performance, reflecting state-of-the-art metrics on real-world datasets.

Tables and figures included in the paper offer quantitative evidence on how the superior protocols enhance the PointNet++ performance from 89.8% to 93.3% when moving from its original evaluation to the RSCNN's protocol on ModelNet40. Similarly, SimpleView achieves up to 93.6% under various settings, displaying its robustness and adaptability to different dataset conditions.

Theoretical Implications and Future Directions

The theoretical implications of this research are particularly salient in encouraging a paradigm shift from solely focusing on novel architectures to considering holistic design principles that incorporate implementation protocols. This refocus can guide the development of future models that optimize protocol elements to attain desired results efficiently.

Furthermore, the unexpected efficacy of a straightforward projection-based model such as SimpleView challenges preconceptions about the need for complex processing to avoid information loss in point cloud data. This finding nudges the domain towards simpler methods that emphasize fewer parameters and operations without significant sacrifices in accuracy or generalization capabilities.

Conclusion

In summary, this paper prompts a re-examination of what constitutes effective design in point cloud classification by illustrating the predominant influence of external factors over the architectures themselves. As a direct consequence, research and development in machine learning models can benefit from leveraging protocol optimizations as much as pursuing advancements in architectural sophistication. Future investigations could explore the extension of these protocol considerations to problems like point segmentation and 3D object detection, potentially enhancing applicable domains' efficiency and accuracy.