| --- |
| tags: |
| - gaussian-splatting |
| - 3d-reconstruction |
| --- |
| |
| # 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). |
|
|
