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README.md

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 [![GitHub](https://img.shields.io/github/license/rayguan97/crossloc3d)](https://github.com/rayguan97/crossloc3d/blob/master/LICENSE)
 
 
-[CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition](https://arxiv.org/abs/2303.17778).
+[CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition](https://arxiv.org/abs/2303.17778)
 <br> Tianrui Guan, Aswath Muthuselvam, Montana Hoover, Xijun Wang, Jing Liang, Adarsh Jagan Sathyamoorthy, Damon Conover, Dinesh Manocha
 
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+# Motivation
+<img src="./resources/diff.png" width="1080">
+<br>
+Representation gap between aerial and ground sources: We use the bounding box with the same color to focus on the same region and highlight the differences between aerial (**left**) and ground (**right**) LiDAR scans. Scopes (<span style="color:cyan">cyan</span>): The aerial scans cover a large region, while ground scans cover only a local area. Coverages (<span style="color:lawngreen">green</span>): The aerial scans cover the top of the buildings, while ground scans cover more details on the ground. Densities (<span style="color:blue">blue</span>): The distribution and density of the points are different because of various scan patterns, effective ranges, and fidelity of LiDARs. Noise Patterns (<span style="color:red">red</span>): The aerial scans have larger noises, as we can see from a bird-eye view and top-down view of a corner of the building.
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+<br>
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+# Network Architecture
+<img src="./resources/network.png" width="1080">
+<br>
+<br>
 
 If you find this project useful in your research, please cite our work: