# Code map

The public API / CLI / Space separation follows the organization of
[TencentARC/GAE-GeometricAutoEncoder](https://github.com/TencentARC/GAE-GeometricAutoEncoder)
(reference inspected 2026-10-15). PURE remains a reconstruction/post-training
project; no GAE generation model or sampling code is included.

| Path | Responsibility |
| --- | --- |
| `pure/hub.py` | `PURE`, `PUREConfig`, strict HF loading, lossless saving |
| `pure/processing.py` | RGB preprocessing and consecutive video frames |
| `pure/pipeline.py` | Shared inference → optional sky masks → exports |
| `pure/sky.py` | Pinned SegFormer RGB sky segmentation |
| `pure/export.py` | Full filtered PLY, preview GLB, videos and camera files |
| `pure/models/` | Pi3 v2 / DA3 Giant / VGGT adapters |
| `pure/_vendor/` | Upstream network components, with original notices |
| `pure/loss.py`, `pure/utils/` | Original public PURE objective and geometry |
| `pure/training/` | Teacher caches, optimization, DDP and checkpoint resume |
| `scripts/demo/reconstruct.py` | Model-folder or Hub inference CLI |
| `scripts/train/train.py` | Training CLI; forwards to the existing tested engine |
| `scripts/data/prepare_cache.py` | Training-cache preparation CLI |
| `scripts/hub/convert_weights.py` | Three lossless model-folder conversions |
| `scripts/hub/upload.py` | Explicit upload of converted model folders |
| `configs/` | Training configurations |
| `app.py` | Gradio GPU Space; uses the same pipeline as the CLI |
| `tests/` | Training, adapter, serialization, preprocessing and export checks |
| `recovery/` | Original recovery evidence; not imported at runtime |

Existing `scripts/train.py`, `scripts/infer.py`, `scripts/prepare_cache.py` and
Pi3 aliases remain available. Training checkpoint keys are unchanged. The new
`PURE` API wraps a backbone, while HF weight files retain the backbone's original
keys. To resume the training CLI from a single converted Safetensors file, use
`--checkpoint hf_models/pi3/model.safetensors` with the matching training config.
Training optimizer state remains in training checkpoints, not model folders.