Summary of Kaninfradet3d:a Road-side Camera-lidar Fusion 3d Perception Model Based on Nonlinear Feature Extraction and Intrinsic Correlation, by Pei Liu (1) et al.
Kaninfradet3D:A Road-side Camera-LiDAR Fusion 3D Perception Model based on Nonlinear Feature Extraction and Intrinsic Correlation
by Pei Liu, Nanfang Zheng, Yiqun Li, Junlan Chen, Ziyuan Pu
First submitted to arxiv on: 21 Oct 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 The paper proposes a novel approach to roadside 3D perception using LiDAR and cameras, building on recent advancements in Kolmogorov-Arnold Networks (KANs). The authors develop Kaninfradet3D, an improved feature extraction and fusion model that leverages KAN Layers and cross-attention mechanisms. This architecture enhances the integration of camera features, addressing issues with abnormal concentration. Compared to benchmarks, Kaninfradet3D demonstrates significant improvements in mean Average Precision (mAP) on both TUMTraf Intersection Dataset and TUMTraf V2X Cooperative Perception Dataset. The results suggest that KANs can effectively improve roadside perception tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help cars see the world around them, especially when they’re parked at the side of the road. It uses special sensors called LiDAR and cameras to get a better view. The team came up with a new model that works better than others because it can combine information from both sources more smoothly. This helps the car’s computer make better decisions about what’s around it. The results show that this new approach is really good at detecting things, especially when compared to other methods. |
Keywords
» Artificial intelligence » Cross attention » Feature extraction » Mean average precision
