kitti-data-preparationValidate KITTI object data and plan Frustum PointNets frustum-pickle preparation from ground-truth or RGB 2D boxes.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this route to validate KITTI Object Detection data, inspect detector rows, and plan the repository's ground-truth or RGB-detection frustum extraction. Do not start a multi-gigabyte conversion until the selected branch passes its layout and schema checks.
Read data formats and identify whether the input uses ground-truth 2D boxes or external RGB detections.
Run the non-destructive validator:
python scripts/validate_kitti_layout.py \
--dataset-root /data/KITTI/object \
--index-file /data/splits/val.txt \
--detector-file /data/detections/val.txt
Omit --detector-file for a ground-truth branch. Add
--require-labels for train/val generation. Use --check-complete only
when every listed frame must be checked rather than sampled.
Read workflows, select train, val, or
val_rgb_detection, and stage output on a filesystem with enough space.
Verify the resulting sequential pickle stream before moving it into a
training or inference workflow. Route training to ../training/SKILL.md.
| Mode | Required evidence | Output basename |
|---|---|---|
| train | training images, Velodyne, calibration, labels, train IDs | `frustum_{caronly |
| val | training images, Velodyne, calibration, labels, val IDs | `frustum_{caronly |
| val RGB detections | training images, Velodyne, calibration, detector rows, val IDs | `frustum_{caronly |
The standard all-mode command is estimated by the repository to generate about
4.7 GB. --car_only changes both the class whitelist and output prefix;
without it, Car, Pedestrian, and Cyclist are selected.
The source targets Python 2 and uses cPickle. A Python-3 port must use an
explicit compatibility import and account for integer-division differences.
Layout and geometry checks are CPU-safe; they do not validate TensorFlow, CUDA,
or v2 custom operators. Read troubleshooting
for malformed detector rows, missing labels, empty frustums, and partial
pickles. Route installation to ../runtime-and-custom-ops/SKILL.md and KITTI
result scoring to ../inference-and-evaluation/SKILL.md.