RemedyGSData: Poisoned 3DGS Training Scenes (eps16–eps150)

RemedyGSData is the large-scale poisoned image dataset built for RemedyGS: Defend 3D Gaussian Splatting Against Computation Cost Attacks (CVPR 2026).

Recent work such as Poison-splat shows that attackers can poison input images to trigger excessive Gaussian densification, dramatically increasing GPU memory, training time, and rendering latency — potentially leading to denial-of-service (DoS) in 3DGS-as-a-service systems. RemedyGSData provides 702 real-world scenes poisoned by the bounded Poison-splat attacker at 7 perturbation budgets (eps ∈ {16, 26, 35, 40, 50, 100, 150}, in units of 1/255), enabling reproducible benchmarking of defenses against computation-cost attacks on 3D Gaussian Splatting.

All scenes are derived from DL3DV-10K and are provided in standard COLMAP format (images/ + sparse/0/), drop-in compatible with vanilla 3DGS training.

Related resources

Resource	Link
Code (detector + purifier + victim benchmark)	github.com/Polly-LYP/RemedyGS
Detector checkpoint (D_ckpt.pth, poisoned-vs-clean image classifier)	huggingface.co/ZincL/RemedyGS-detector
Purifier checkpoint (G_ckpt.pth.tar, image recovery generator)	included in the GitHub repo (ckpt/)

Dataset structure

eps16~eps150/
├── eps16_<scene_hash>.tar      # 780→600 scenes attacked with eps=16/255
├── eps26_<scene_hash>.tar      # 18 scenes attacked with eps=26/255
├── eps35_<scene_hash>.tar      # 18 scenes, eps=35/255
├── eps40_<scene_hash>.tar      # 18 scenes, eps=40/255
├── eps50_<scene_hash>.tar      # 16 scenes, eps=50/255
├── eps100_<scene_hash>.tar     # 15 scenes, eps=100/255
└── eps150_<scene_hash>.tar     # 17 scenes, eps=150/255

Each tar contains one scene folder:

epsXX_<scene_hash>/
├── images/            # final poisoned images (frame_00001.png ... frame_NNNNN.png, RGB PNG)
├── images_15000/      # poisoned images at attack iteration 15000
├── images_20000/      # poisoned images at attack iteration 20000
├── images_25000/      # poisoned images at attack iteration 25000
└── sparse/0/          # COLMAP reconstruction: cameras.bin, images.bin, points3D.bin, points3D.ply

Attack configuration (Poison-splat bounded attacker): --adv_iters 30000 --adv_image_search_iters 25, using the clean 3DGS model trained for 30k iterations as the proxy. The eps16 split (600 scenes) is the main benchmark used in the RemedyGS paper; eps26–eps150 form the perturbation-budget ablation (same 20-scene subset, with 1 scene unavailable for eps50/eps100).

Statistics

eps (×1/255)	scene tars	fully complete*	images/ files
16	600	600	201,270
26	18	14	6,047
35	18	12	6,048
40	18	12	6,206
50	16	10	5,288
100	15	7	5,125
150	17	9	5,879
total	702	664	235,863

* "fully complete" = all four image snapshots (images/, images_15000/, images_20000/, images_25000/) contain the same number of frames. Partial scenes have one or more snapshots missing (attack run interrupted); see scene_metadata.csv for per-scene details. Total dataset size: ~975 GB.

Usage

Download (whole repo or a subset):

pip install -U huggingface_hub

# everything (~975 GB)
hf download ZincL/RemedyGS --repo-type dataset

# or a single scene
hf download ZincL/RemedyGS eps16~eps150/eps16_<scene_hash>.tar --repo-type dataset

Extract a scene and train vanilla 3DGS on it:

tar -xf eps16~eps150/eps16_<scene_hash>.tar     # -> eps16_<scene_hash>/

python train.py -s eps16_<scene_hash> -m output/eps16_<scene_hash>   # standard 3DGS

Evaluate a defense (e.g. RemedyGS) on a poisoned scene. RemedyGS runs detector first, purifier second — the detector (DetectorNet, a poisoned-vs-clean image classifier; not to be confused with the GAN discriminator, which is only used during purifier training) flags poisoned images, and only those are recovered by the purifier:

# Step 1 — detector: classify each input image as poisoned (1) or clean (0)
python inference/detector_inference.py \
  --image_dataset_path . --dataset_list scene_list.txt \
  --output_path ./output --load_existing_model_path ckpt/D_ckpt.pth

# Step 2 — purifier: recover benign images from the poisoned ones
python inference/inference.py \
  --image_dataset_path . --dataset_list scene_list.txt \
  --output_path ./output --load_existing_model_path ckpt/G_ckpt.pth.tar

Metadata

scene_metadata.csv — one row per scene tar:

column	description
folder_name	scene folder / tar name, epsXX_<scene_hash>
scene_id	original DL3DV-10K scene hash
eps	perturbation budget (×1/255)
source	origin split (sampled_100 main benchmark, 1K ablation)
num_images	number of frames in images/
complete	whether all four snapshots are present and consistent

License & terms

Scenes are derived from DL3DV-10K; by using this dataset you must also comply with the original DL3DV-10K license terms. This dataset is released for non-commercial research use (CC BY-NC 4.0). The poisoned images were generated with the open-source Poison-splat attacker.

Citation

@article{li2025remedygs,
  title={RemedyGS: Defend 3D Gaussian Splatting Against Computation Cost Attacks},
  author={Li, Yanping and Liu, Zhening and Li, Zijian and Lin, Zehong and Zhang, Jun},
  journal={arXiv preprint arXiv:2511.22147},
  year={2025}
}