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Track 4: 3D Point Cloud Classification

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.

Baseline Algorithm

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

Metrics

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"