---

name: data-preparation description: "Prepare and validate Neuralangelo datasets, transforms.json metadata, and data configs before training." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Neuralangelo Data Preparation

Use this sub-skill when the task is to prepare inputs for Neuralangelo from a self-captured video, an existing COLMAP reconstruction, DTU-style data, or Tanks-and-Temples-style data. The expected handoff is a dataset directory with usable images, an Instant-NGP-style transforms.json, and a Neuralangelo YAML config patch.

Do not use this sub-skill to run training, tune optimization, resume checkpoints, or choose GPU/memory settings; reroute those tasks to the sibling training-and-configs sub-skill. Do not use it for isosurface or textured mesh extraction; reroute to mesh-extraction.

Start Here

1. Identify the source type:
   • video: a self-captured video that still needs frame extraction and COLMAP.
   • colmap: an already reconstructed scene with images and sparse camera data.
   • dtu: DTU/NeuS-style scans with image/ folders and camera parameter archives.
   • tnt: Tanks-and-Temples-style scenes with images, COLMAP pose logs, alignment files, and point clouds.
2. Choose scene_type before generating metadata or configs:
   • object: object-centric turntable or bounded object; pose-derived bounds; lower coarse-to-fine start.
   • outdoor: building/large scene; point-derived bounds unless cameras are strongly concentric; background model remains enabled.
   • indoor: room-scale scene; point-derived bounds; inside-out SDF setting and no background samples.
3. Produce or collect transforms.json at the dataset root.
4. Validate transforms.json before launching any expensive training.
5. Generate a YAML config patch from the actual image dimensions and optional exposure/white-balance choice.
6. Inspect camera poses and bounding regions. If bounds are wrong, prefer editing data.readjust.center and data.readjust.scale in the generated config before changing training code.

Bundled Helpers

Run helper scripts from this sub-skill directory, or replace scripts/... with the resolved path to the bundled script.

python scripts/plan_preprocessing_commands.py \
  --mode video \
  --sequence-name garden_scan \
  --video /data/videos/garden_scan.mp4 \
  --downsample-rate 2 \
  --scene-type object \
  --data-dir /data/neuralangelo/garden_scan_ds2

python scripts/validate_transforms_json.py \
  --transforms /data/neuralangelo/garden_scan_ds2/transforms.json \
  --data-dir /data/neuralangelo/garden_scan_ds2 \
  --camera-centers-csv /tmp/garden_scan_camera_centers.csv

python scripts/generate_config_from_images.py \
  --data-dir /data/neuralangelo/garden_scan_ds2 \
  --sequence-name garden_scan \
  --scene-type object \
  --auto-exposure-wb \
  --output /data/neuralangelo/garden_scan.yaml

The helpers are safe: they plan or validate files and do not import Neuralangelo source code, launch training, run COLMAP, download datasets, or extract meshes.

Reference Map

• references/workflows.md: end-to-end data workflows, prerequisites, scene_type effects, and pose/bounds inspection planning.
• references/data-formats.md: expected directory layouts, transforms.json schema, coordinate conventions, bounding fields, and generated YAML fields.
• references/troubleshooting.md: common validation failures, COLMAP/data caveats, bounding-region symptoms, and dataset-specific fixes.

Handoff Checklist

A prepared data handoff should state:

• dataset root and image subdirectory name;
• source type and scene_type used for metadata/config generation;
• number of frames/images validated;
• whether auto_exposure_wb / appearance embeddings were enabled;
• sphere_center, sphere_radius, and whether aabb_range is present;
• any manual data.readjust.center or data.readjust.scale recommendation;
• validation errors/warnings that remain unresolved.