Summary of Enhancing Sampling Protocol For Point Cloud Classification Against Corruptions, by Chongshou Li et al.
Enhancing Sampling Protocol for Point Cloud Classification Against Corruptions
by Chongshou Li, Pin Tang, Xinke Li, Yuheng Liu, Tianrui Li
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 addresses a critical issue in 3D point cloud learning by proposing an enhanced sampling protocol called PointSP. The existing protocols, such as Farthest Point Sampling (FPS) and Fixed Sample Size (FSS), are vulnerable to corruptions like sensor noise, which can pose significant safety risks in applications like autonomous driving. To mitigate these issues, PointSP incorporates key point reweighting to reduce outlier sensitivity and ensure the selection of representative points. It also introduces a local-global balanced downsampling strategy for scalable and adaptive sampling while maintaining geometric consistency. Furthermore, PointSP uses a lightweight tangent plane interpolation method to preserve local geometry and enhance point cloud density. Unlike learning-based approaches that require additional model training, PointSP is architecture-agnostic, requiring no extra learning or modification to the network. This enables seamless integration into existing pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Point clouds are used in many applications like autonomous driving, but they often have noise or corruptions that can affect their accuracy. The current way of sampling point clouds doesn’t work well when there’s noise, so this paper proposes a new method called PointSP to fix this problem. PointSP is better at handling noisy data and it works with any existing network architecture without needing extra training. This makes it easier to use in real-world applications. |
