SalientGS: Unified SfM-to-3DGS with Importance-Guided MCMC Gaussian Allocation

SalientGS is an end-to-end pipeline for reconstructing 3D Gaussian Splatting (3DGS) scenes from unordered images. It combines Fisher Vector retrieval with MST connectivity, first-order SfM, joint pose-appearance refinement, and importance-guided MCMC Gaussian allocation.

Project page: https://six-bit-tx.github.io/SalientGS/

Paper: paper/main.pdf · Supplementary: paper/supplementary.pdf

Highlights

• Across Mip-NeRF 360, Deep Blending, and Tanks & Temples, the released 13-scene verification gives the best equal-benchmark macro-average PSNR/SSIM/LPIPS (27.65 dB / 0.876 / 0.147) and the fastest end-to-end runtime (10.62 minutes) among the methods compared in the paper; failed scenes remain included in the aggregate.
• Importance-guided MCMC reallocates a fixed Gaussian budget toward persistent multi-view underfit regions.
• The unified pipeline jointly refines SfM poses and Gaussian appearance with photometric and reprojection losses.
• Fisher Vector retrieval plus MST connectivity provides a fast unordered-image front end.
• The released implementation includes the FastMap CUDA extension, evaluation scripts, and command-line entry points.

Installation

The released code targets Python 3.10 or newer, PyTorch with CUDA support, COLMAP for SIFT feature extraction, and gsplat.

Create/activate a CUDA-enabled Python environment with PyTorch first, then run:

git clone https://github.com/Six-Bit-TX/SalientGS.git
cd SalientGS
./scripts/setup_salientgs.sh

The setup script checks PyTorch/CUDA and COLMAP, builds FastMap for the visible GPU architecture (including Blackwell), installs the Python dependencies, and loads the CUDA extension as a final check.

The Python package dependencies are pinned or listed in pyproject.toml. The FastMap extension requires a CUDA-capable build environment.

Usage

DATA=/path/to/dataset

# Feature extraction and Fisher Vector retrieval
sgs-feat --image_dir "$DATA/images" --output_dir "$DATA"

# First-order SfM
sgs-sfm --headless \
  --database "$DATA/database.db" \
  --image_dir "$DATA/images" \
  --output_dir "$DATA"

# Joint 3DGS training
sgs-joint --data_path "$DATA"

The expected dataset layout is:

dataset/
├── images/
├── database.db
└── sparse/0/
    ├── cameras.txt
    ├── images.txt
    └── points3D.txt

The scripts/ directory contains benchmark, runtime, ETH3D pose-evaluation, and GLOMAP comparison scripts.

The exact per-scene measurements from the July 2026 13-scene verification run are archived in results/paper_reproduction_seed42.csv, with aggregate comparisons and protocol notes in results/README.md.

The complete LaTeX sources and all required figure assets are included in paper/; both the main paper and supplementary material can be compiled independently from that directory.

One-click paper reproduction

The reproduction runner is non-destructive: source images are symlinked into a separate work directory, and databases, sparse models, checkpoints, logs, and CSV/JSON summaries are written there. Completed stages are resumed automatically.

# Short checks on four representative scenes (7K training steps).
./scripts/reproduce_paper.sh \
  --dataset-root /path/to/data \
  --work-root /path/to/salientgs_runs \
  --profile representative

# Full 13-scene protocol (30K steps, 1.5M Gaussian cap).
./scripts/reproduce_paper.sh \
  --dataset-root /path/to/data \
  --work-root /path/to/salientgs_runs \
  --profile full

# Fair vanilla-MCMC control using the same SfM outputs and seed.
./scripts/reproduce_paper.sh \
  --dataset-root /path/to/data \
  --work-root /path/to/salientgs_runs \
  --profile full --method vanilla --stages train

Use --scenes garden counter to split a long run across invocations. Summaries are merged at PROFILE/summary_guided.csv (or summary_vanilla.csv). For an isolated training diagnostic using the reference sparse model, add --reference-sfm --stages train; this is not an end-to-end paper result.

Reproducibility notes

The paper reports a 1.5M Gaussian budget, 30K joint-training iterations, Fisher Vector retrieval with 64 GMM components and top-20 neighbors, robust score quantiles (0.05, 0.90), an importance threshold of 5, redundancy threshold of 0.9, opacity mixing of 0.05, and 10 views per score update. Scores begin after a 3K pose warmup and are recomputed every 500 iterations.

Datasets, pretrained weights, and generated experiment outputs are not included in this repository. Please follow the licenses and terms of the respective datasets and third-party dependencies.

License

The code in this repository is released under CC BY-NC 4.0. Third-party components retain their own licenses.

Citation

@inproceedings{xiong2026salientgs,
  author    = {Tianyu Xiong and Rui Li and Suning Ge and Jiaqi Yang},
  title     = {SalientGS: Unified SfM-to-3DGS with Importance-Guided MCMC Gaussian Allocation},
  booktitle = {Proceedings of the 34th ACM International Conference on Multimedia},
  year      = {2026}
}