tags:
- gaussian-splatting
- 3d-reconstruction
LonGaussian
Pretrained weights for LonGaussian: Tracking Foundation Priors for Long-Context Feedforward Gaussian Reconstruction.
Paper · Code · Project page
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 or use 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:
python scripts/download_assets.py weights --output /path/to/weights
Run the full reconstruction pipeline using the downloaded checkpoint:
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.