| # 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. |
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