Summary of Uncertainty Estimation and Out-of-distribution Detection For Lidar Scene Semantic Segmentation, by Hanieh Shojaei et al.
Uncertainty Estimation and Out-of-Distribution Detection for LiDAR Scene Semantic Segmentation
by Hanieh Shojaei, Qianqian Zou, Max Mehltretter
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 A novel method is proposed to enhance the capabilities of autonomous vehicles and robots in navigating new environments by accurately interpreting their surroundings. The approach relies on LiDAR scene segmentation, out-of-distribution (OOD) obstacle detection, and uncertainty computation. A Gaussian Mixture Model (GMM) is fitted to the feature space of a single deterministic model to distinguish between in-distribution (ID) and OOD samples. This eliminates the need for an additional OOD training set. The method also quantifies both epistemic and aleatoric uncertainties using the feature space, demonstrating superior performance compared to deep ensembles and logit-sampling in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a self-driving car that can navigate through new places without getting confused. To make this possible, researchers developed a way to help robots and cars understand their surroundings better. They used a special kind of math called Gaussian Mixture Model (GMM) to figure out when something is normal or unusual. This helps the car avoid obstacles and makes it more confident in its decisions. The new method works better than other approaches in real-life situations, which means it could be useful for self-driving cars and robots in the future. |
Keywords
» Artificial intelligence » Mixture model
