Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data (1908.04616v2)

Abstract: Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classification methods have reported state-of-the-art performance on CAD model datasets such as ModelNet40 with high accuracy (~92%). Despite such impressive results, in this paper, we argue that object classification is still a challenging task when objects are framed with real-world settings. To prove this, we introduce ScanObjectNN, a new real-world point cloud object dataset based on scanned indoor scene data. From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions. We identify three key open problems for point cloud object classification, and propose new point cloud classification neural networks that achieve state-of-the-art performance on classifying objects with cluttered background. Our dataset and code are publicly available in our project page https://hkust-vgd.github.io/scanobjectnn/.

• The paper introduces ScanObjectNN, a real-world point cloud dataset that captures challenges like clutter and partial occlusions.
• The paper proposes a background-aware model that jointly learns classification and segmentation to enhance performance in noisy environments.
• Results reveal a significant drop in model accuracy from synthetic to real-world data, underscoring the need for improved generalization.

Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

The paper "Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data" presents a critical reassessment of current techniques in point cloud classification, particularly emphasizing the challenges posed by real-world data. While existing methods demonstrate high accuracy on synthetic datasets such as ModelNet40, their performance declines when applied to realistic settings. This discrepancy highlights a need to bridge the gap between synthetic and real-world data.

Contributions

The paper introduces ScanObjectNN, a new real-world point cloud dataset derived from scanned indoor scenes. Unlike synthetic datasets, ScanObjectNN encompasses challenges such as background clutter and partial occlusions typical in real-world scenarios. The dataset includes approximately 15,000 objects categorized into 15 common classes, offering various degrees of perturbation to simulate practical challenges.

Additionally, the authors propose new point cloud classification models that integrate a background-aware network. These models jointly learn classification and segmentation tasks within a single framework, improving classification in cluttered environments.

Benchmarking and Insights

The paper provides a comprehensive benchmark of existing object classification techniques on both synthetic (e.g., ModelNet40) and real-world data (e.g., ScanObjectNN). The results indicate a substantial gap in performance when transitioning from synthetic to real-world datasets, with accuracy dropping to less than 50% for several state-of-the-art models. This issue underscores the limitations inherent in current methodologies developed primarily on synthetic data.

The evaluation also explores the impact of background clutter and object perturbation on classification accuracy. The addition of real-world features, such as noise and incomplete shapes, further challenges these models, revealing potential areas for further research.

Open Problems and Proposed Solutions

The paper identifies three central open problems:

1. Generalization Across Domains: Existing models trained on synthetic data exhibit poor generalization to real-world data due to structural and noise differences. The paper proposes training on real-world data to enhance adaptability.
2. Effect of Background Clutter: Background elements, while contextually useful, often introduce noise. The proposed models, with joint segmentation, aim to discern and utilize context effectively.
3. Handling Partiality and Occlusions: Real-world data typically include partial objects due to occlusions. Leveraging part segmentation frameworks could assist in improving classification under these conditions.

Implications and Future Directions

The introduction of ScanObjectNN presents a fundamental step towards enhancing point cloud classification robustness. The dataset provides a more accurate and challenging benchmark, encouraging the development of models that can handle the complexities of real-world data.

Practically, this emphasizes the need for enhanced 3D perception systems in applications such as robotics and autonomous vehicles, which encounter real-world clutter and partial views regularly. Theoretically, this prompts a reevaluation of network architectures to improve feature extraction from noisy and occluded data sets.

Future research should focus on developing generalized models bridging the gap between synthetic and real-world domains, perhaps integrating advanced data augmentation techniques or transfer learning approaches to achieve this goal.

In conclusion, this paper contributes significantly by not only identifying critical gaps in current research but also by providing the datasets and initial models necessary to address these challenges, fostering progress toward robust and reliable 3D object classification in real-world environments.