Release pretrained LonGaussian checkpoint

README.md

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+# LonGaussian
+
+Pretrained weights for **LonGaussian: Tracking Foundation Priors for
+Long-Context Feedforward Gaussian Reconstruction**.
+
+[Paper](https://arxiv.org/abs/2610.23250) ·
+[Code](https://github.com/hustvl/LonGaussian) ·
+[Project page](https://abearkeeper.github.io/LonGaussian.github.io/)
+
+## Checkpoint
+
+`stage2b.pt` provides the pretrained Gaussian modules for LonGaussian.
+Use it with the standard `merge_mode=neural` reconstruction pipeline.
+
+| File | Size | SHA256 |
+| --- | --- | --- |
+| `stage2b.pt` | 272,167,298 bytes (272.2 MB / 259.6 MiB) | `9f49fab3604ac174e323c8d74bdc44966e257f3b6be32ef35c338f6fb0bb1feb` |
+
+The file contains the Gaussian decoder, Gaussian attribute head and learned
+track merger. Frozen LoGeR, AllTracker and sky-segmentation weights are obtained
+separately from their original hosts. This is a PyTorch checkpoint for the
+LonGaussian codebase, not a self-contained Transformers model.
+
+## Download and run
+
+Download [stage2b.pt](https://huggingface.co/abkp/LonGaussian/resolve/main/stage2b.pt?download=true)
+or use Python:
+
+```python
+from huggingface_hub import hf_hub_download
+
+checkpoint = hf_hub_download(
+    repo_id="abkp/LonGaussian",
+    filename="stage2b.pt",
+    local_dir="/path/to/weights",
+)
+```
+
+Install the code repository's dependencies, then download the frozen models:
+
+```bash
+python scripts/download_assets.py weights --output /path/to/weights
+```
+
+Run the full reconstruction pipeline using the downloaded checkpoint:
+
+```bash
+INPUT=/path/to/calibrated_scene NUM_FRAMES=24 OUTPUT_DIR=/path/to/new_inference \
+MODEL_NAME=/path/to/weights/LoGeR_star/latest.pt \
+GAUSSIAN_HEAD=/path/to/weights/stage2b.pt \
+ALLTRACKER_CHECKPOINT=/path/to/weights/alltracker.pth \
+SKY_MASK_ONNX=/path/to/weights/skyseg.onnx \
+bash scripts/infer_longaussian.sh
+```
+
+Training datasets and Stage-I/IIa checkpoints are not required for inference.
+Inputs must follow the repository's calibrated image-sequence format. Camera
+poses are predicted by the model. See the repository for data preparation and
+the complete evaluation protocol.
+
+## Hardware and scope
+
+CUDA is required by the full reconstruction/rendering pipeline. The validated
+reference hardware is an RTX 3090 with 24 GB VRAM. Memory usage depends on
+input resolution, sequence length, scene content and runtime settings.
+
+LonGaussian targets static-scene reconstruction. Dynamic objects, inaccurate
+camera poses and distant loop revisits may cause artifacts. Frozen third-party
+weights are downloaded separately; see the original sources and the code
+repository's [third-party notices](https://github.com/hustvl/LonGaussian/blob/main/THIRD_PARTY_LICENSES.md).

SHA256SUMS

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+9f49fab3604ac174e323c8d74bdc44966e257f3b6be32ef35c338f6fb0bb1feb  stage2b.pt

checkpoint.json

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+{
+  "format": "longaussian_checkpoint_v1",
+  "file": "stage2b.pt",
+  "size_bytes": 272167298,
+  "sha256": "9f49fab3604ac174e323c8d74bdc44966e257f3b6be32ef35c338f6fb0bb1feb",
+  "modules": [
+    "gaussian_decoder",
+    "gaussian_head",
+    "neural_track_merger"
+  ],
+  "tensor_count": 128,
+  "merge_mode": "neural",
+  "frozen_dependencies_bundled": false,
+  "code_url": "https://github.com/hustvl/LonGaussian",
+  "paper_url": "https://arxiv.org/abs/2610.23250",
+  "usage": "GAUSSIAN_HEAD=/path/to/weights/stage2b.pt bash scripts/infer_longaussian.sh (also supply the documented input and frozen dependencies)"
+}

stage2b.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9f49fab3604ac174e323c8d74bdc44966e257f3b6be32ef35c338f6fb0bb1feb
+size 272167298