---

name: kitti-data-preparation description: "Validate KITTI object data and plan Frustum PointNets frustum-pickle preparation from ground-truth or RGB 2D boxes." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

KITTI data preparation

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.

Workflow

1. Read data formats and identify whether the input uses ground-truth 2D boxes or external RGB detections.

2. 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.

3. Read workflows, select train, val, or val_rgb_detection, and stage output on a filesystem with enough space.

4. Verify the resulting sequential pickle stream before moving it into a training or inference workflow. Route training to ../training/SKILL.md.

Source-equivalent modes

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.

Boundaries

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.