# Release scope

This document records what can be run from this snapshot and what requires additional implementation or experiment artifacts.

## Implemented and directly checkable

- Deterministic synthetic metadata example and generated image/depth files.
- Normalized candidate selection and covered-cell metric utilities.
- Annotated oracle-gap and basic render-file checks.
- Blender/GLB/PLY pipeline sources and native output summarizers.
- Author attribution, dataset metadata snapshot and code integrity tools.

## Important interface differences

`pipelines/run_blend_pipeline.py::select_next_frame` ranks candidates by unoccupied projected pixels minus a fixed overlap penalty. It does not compare candidate probe depths with historical depths in the way required for the paper's depth-disagreement term. Its exported `selected_frames.json` is not the normalized tiny metadata schema.

`scripts/select_views.py` is an independent example based on supplied cell sets and static priors, with an extra 0.15 probe-coverage term. Its sample `oracle_gain` numbers are supplied annotations, not dynamically rendered exact marginal gains. The synthetic image/depth and simplified pose files validate I/O only.

Published Blender pose exports declare camera-to-world rotations and absolute positions; users must reconcile conventions before projection. This release does not relabel or transform already published data.

## Not included as complete reproductions

- Full matched 900-scene world-voxel baseline sweep and scene-bootstrap confidence intervals.
- ERP-LVSM/LoRA training, checkpoints, exact training lists and held-out Replica evaluation.
- Full exact-greedy oracle diagnostics and all rebuttal experiment drivers.
- Executable TartanGround/OB3D re-encoding workflows and all upstream instance mappings.
- Real per-step candidate/selection provenance and full failure-case sequences for the hosted dataset.

Implementing a stub or generating simulated CSVs would not make these experiments reproducible. This repository does not claim that the no-data test verifies paper numbers. Historical H100 inventories record hashes of an earlier local population and are not a public data release manifest or evidence that every artifact is hosted.