Summary of Point-gn: a Non-parametric Network Using Gaussian Positional Encoding For Point Cloud Classification, by Marzieh Mohammadi and Amir Salarpour
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
by Marzieh Mohammadi, Amir Salarpour
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Point-GN, a novel non-parametric network for efficient and accurate 3D point cloud classification. Unlike traditional deep learning models, Point-GN relies on non-learnable components like Farthest Point Sampling (FPS), k-Nearest Neighbors (k-NN), and Gaussian Positional Encoding (GPE) to extract local and global geometric features. This design eliminates the need for additional training while maintaining high performance, making it suitable for real-time applications with limited resources. The authors evaluate Point-GN on two benchmark datasets, ModelNet40 and ScanObjectNN, achieving classification accuracies of 85.29% and 85.89%, respectively, while reducing computational complexity. Compared to existing non-parametric methods, Point-GN outperforms them while matching the performance of fully trained models with zero learnable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to classify 3D point clouds quickly and accurately. Unlike other approaches that need to be trained, this method uses special components to extract important features from the data. This makes it perfect for real-time applications where computers are limited by resources. The authors tested their method on two different datasets and achieved impressive results, outperforming other non-parametric methods while using fewer resources. |
Keywords
» Artificial intelligence » Classification » Deep learning » Positional encoding
