Datasets:
license: cc-by-nc-4.0
task_categories:
- image-to-3d
tags:
- 3dgs
- gaussian-splatting
- adversarial-attack
- poison-splat
- remedygs
- dl3dv
- 3d-reconstruction
- security
size_categories:
- 100K<n<1M
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}
}