Track 4 is the 3D Point Cloud Classification track. The goal is to classify (semantically segment) point clouds on a per point basis. The classes are:
| Class Index | Class Description |
|---|---|
| 2 | Ground |
| 5 | High Vegetation |
| 6 | Building |
| 9 | Water |
| 17 | Bridge Deck |
Additionally, some of the ground truth points are marked with a 0 class. This represents unlabeled data, and points with this label will be ignored for metrics purposes.
For the baseline algorithm, a PointNet++ (aka PointNet2) model was updated with modifications to support splitting/recombining large scenes. For details on setting up/running the model, see pointnet2/dfc/README.md
To run the metrics code, it is easiest to use the same docker container that is used for the model, though it is not necessary. Example command:
docker run -it --rm \
-v /path/to/data:/data \
-v /path/to/metrics_code_folder:/metrics \
dfc_pointcloud bash -c \
"python /metrics/track4-metrics.py -g /data/ground_truth -d /data/output_data | tee /data/output_data/metrics.txt"