WeikaiTan/Toronto-3D
A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
This project provides a substantial dataset of urban roadway scans captured by mobile LiDAR, offering detailed 3D point clouds with 10 attributes and classifications for 8 distinct object types like roads, buildings, and cars. It's designed to help researchers and engineers develop and test algorithms that automatically identify and categorize features within complex city environments from LiDAR data.
291 stars.
Use this if you are developing or evaluating algorithms for autonomously identifying and segmenting urban infrastructure and elements from mobile LiDAR point clouds.
Not ideal if you need a dataset for indoor environments, aerial imagery analysis, or object detection tasks that don't involve semantic segmentation of 3D point clouds.
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291
Forks
32
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Last pushed
Oct 30, 2025
Commits (30d)
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