Summary of Towards Explainable Lidar Point Cloud Semantic Segmentation Via Gradient Based Target Localization, by Abhishek Kuriyal et al.
Towards Explainable LiDAR Point Cloud Semantic Segmentation via Gradient Based Target Localization
by Abhishek Kuriyal, Vaibhav Kumar
First submitted to arxiv on: 19 Feb 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 introduces pGS-CAM, a novel method for generating saliency maps in neural network activation layers. Specifically, it focuses on Semantic Segmentation (SS) of LiDAR point clouds, a crucial task for applications like urban planning and autonomous driving. Building upon Grad-CAM, pGS-CAM uses gradients to highlight local importance, making it robust and effective across various datasets (SemanticKITTI, Paris-Lille3D, DALES) and 3D deep learning architectures (KPConv, RandLANet). The method effectively accentuates feature learning in intermediate activations of SS architectures by highlighting the contribution of each point. This allows for better understanding of how SS models make predictions and identifying areas for improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can segment 3D point clouds from LiDAR scans. It’s like teaching a computer to look at a city and identify different buildings, roads, and objects. The researchers created a new way to show which parts of the computer’s thinking are most important for making these decisions. They tested this method on several datasets and showed it works well. This can help us make better computers that can understand 3D data. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Semantic segmentation
