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data-preparation

data-preparation

Guides Nerfstudio dataset conversion, transforms.json validation, camera conventions, device capture modes, and dataparser selection.

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Skill-Dokumentation

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name: data-preparation description: "Guides Nerfstudio dataset conversion, transforms.json validation, camera conventions, device capture modes, and dataparser selection." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data Preparation

Use this route before training when the task is about custom captures, camera poses, transforms.json, ns-process-data, or choosing a dataparser.

What this route covers

  • Converting images, videos, Polycam, Record3D, Metashape, RealityCapture, ODM, and Project Aria style captures into the Nerfstudio dataset layout.
  • Validating the Nerfstudio transforms*.json schema, image/depth/mask paths, split lists, and camera conventions.
  • Distinguishing COLMAP/FFmpeg/hloc/external-tool requirements from package-level Python issues.
  • Choosing between nerfstudio-data, blender-data, minimal-parser, colmap, and other built-in dataparser names.

What this route excludes

  • Training method choice and config overrides: use training-and-configs after the dataset validates.
  • Dataset download commands: use cli-workflows first, because downloads need network and disk checks.
  • Custom dataparser packaging: use api-extension.

Read/run these bundled files

Safe workflow

  1. Identify the capture type and external prerequisites.
  2. If using images/video, confirm COLMAP and FFmpeg unless an existing sparse model makes --skip-colmap valid.
  3. Run the appropriate ns-process-data command into a new output directory.
  4. Validate the output directory with the bundled validator.
  5. Pass the dataset directory to ns-train through training-and-configs.

Key reminders

  • Nerfstudio uses OpenGL/Blender-style camera coordinates; COLMAP/OpenCV axes differ and are converted by the processing/dataparser code.
  • Image paths in transforms*.json are relative to the dataset directory unless absolute paths are intentionally used.
  • Depth images are specified per frame with depth_file_path and are interpreted in millimeters by default.
  • Masks must be one-channel black/white images, same resolution as RGB, and must be present for all frames if used.
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