Update README.md

README.md

@@ -8,10 +8,12 @@ Semantic segmentation of point clouds is time-consuming, especially for large da
 The goal of this task is to achieve semi-automatic (supervised) or automatic (unsupervised) semantic segmentation of the LiDAR point cloud dataset of one of the stories of the HFT building (Bau 4) and/or its exterior urban areas (streets, signs, sidewalks, etc.).
 
 ## Targets:
-Classify the building's LiDAR point cloud data into the following classes: wall, floor, ceiling, window, door, column, beam, stairs/ramps, roof, furniture, opening/passageway, and others. Alternatively, classify the exterior urban area's LiDAR data into the following classes: building, ground, road, sidewalk, signage, vegetation, vehicles, water, and others.
-Visualize the data as separate, labeled point clouds (LAS, PTS, XYZ formats).
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-Visualize in map applications.  
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+-Classify the building's LiDAR point cloud data into the following classes: wall, floor, ceiling, window, door, column, beam, stairs/ramps, roof, furniture, opening/passageway, and others. Alternatively, classify the exterior urban area's LiDAR data into the following classes: building, ground, road, sidewalk, signage, vegetation, vehicles, water, and others.
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+-Visualize the data as separate, labeled point clouds (LAS, PTS, XYZ formats).  
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+-Visualize in map applications.  
 
 ## Data Needs:
 - LiDAR scans of the HFT campus.