Summary of Tiletracker: Tracking Based Vector Hd Mapping Using Top-down Road Images, by Mohammad Mahdavian et al.
TileTracker: Tracking Based Vector HD Mapping using Top-Down Road Images
by Mohammad Mahdavian, Mo Chen, Yu Zhang
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 a novel tracking-based HD mapping algorithm that leverages top-down road images, referred to as tile images. The authors modify the BEVFormer layers to generate bird’s eye view (BEV) masks from tile images, which are then used to generate divider and boundary lines. The model is tested on both color and intensity images, with quantitative and qualitative results demonstrating its performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make detailed maps of roads using pictures taken from directly above. Instead of using special cameras that take pictures from the side, they use pictures that look like tiles. This can help create more accurate maps of roads. The algorithm works by turning the tile images into bird’s eye view masks, which are then used to draw lines on the map. |
Keywords
* Artificial intelligence * Tracking
