| --- |
| 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)](https://github.com/Polly-LYP/RemedyGS). |
|
|
| Recent work such as [Poison-splat](https://github.com/jiajun-cheng/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](https://github.com/DL3DV-10K/Dataset) 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](https://github.com/Polly-LYP/RemedyGS) | |
| | Detector checkpoint (`D_ckpt.pth`, poisoned-vs-clean image classifier) | [huggingface.co/ZincL/RemedyGS-detector](https://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): |
|
|
| ```bash |
| 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: |
|
|
| ```bash |
| 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: |
|
|
| ```bash |
| # 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
