At-access transforms & augmentations for apairo datasets — numpy-in/numpy-out, applied lazily at read time
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Updated
Jul 23, 2026 - Python
At-access transforms & augmentations for apairo datasets — numpy-in/numpy-out, applied lazily at read time
Label synchronized channels — 3D point clouds, camera images — into a top-down BEV grid or per-point labels.
Offline preprocessors for Apairo — ground filtering, odometry, segmentation & derived channels, computed once and persisted
Annotate lidar point clouds in 3D — run a model, click one cluster, label the whole group at once.
Rosbag extractor working with apairo (apairo config, preprocess, ... ) with a CLI interface
Robotics datasets prepared with apairo, published on the Hugging Face Hub — doubling as end-to-end usage examples
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