--- license: cc-by-nc-4.0 task_categories: - image-classification tags: - 3d-gaussian-splatting - data-poisoning - poison-splat - benchmark pretty_name: TextureBench-3DGS size_categories: - 1K **Note.** This is an anonymised copy of the benchmark released for the double-blind review process. It contains no author > information; the full release with attribution, licence details and accompanying code will be published after the review. TextureBench-3DGS is a benchmark of **28 real outdoor scenes with dense natural texture** (gravel, grass, leaves, brick, concrete, tiles, foliage), each captured as a multi-view photo set suitable for 3D Gaussian Splatting (3DGS), together with a **Poison-Splat poisoned twin** of every scene. Clean scenes are high-frequency by nature, so they stress detectors that rely on a high-frequency or anisotropy prior to recognise Poison-Splat inputs. The benchmark is meant for *evaluating* detectors and purifiers; it was never used to train or calibrate any detector reported alongside it. ## Layout ``` TextureBench/ `-- / |-- clean/ | |-- images/ # Clean views | |-- sparse/0/ # COLMAP reconstruction | `-- config.yaml # Capture and preprocessing `-- ps_eps16/ |-- images/ # Poisoned views |-- sparse/0/ # COLMAP reconstruction `-- config.yaml # Source images, attack parameters, generation settings ``` 28 scenes, the same file names in `clean/images` and `ps_eps16/images`. ### `clean/` * `images/`: the released clean views. EXIF removed. **These are the images detectors are scored on.** * `sparse/0/`: COLMAP model (`cameras.bin`, `images.bin`, `points3D.bin`). To train a 3DGS model on the clean scene, follow the standard 3DGS `convert.py` route, i.e. undistort first: ```bash colmap image_undistorter --image_path /clean/images --input_path /clean/sparse/0 \ --output_path /clean_undistorted --output_type COLMAP python train.py -s /clean_undistorted -r -1 ``` ### `ps_eps16/` * `images/`: the Poison-Splat poisoned views, exactly as emitted by the attacker (bounded attack, ε = 16/255, 12 000 attack iterations, proxy model = the clean 3DGS reconstruction). **These are the images detectors are scored on for the poisoned class.** * `sparse/0/`: COLMAP model (`cameras.bin`, `images.bin`, `points3D.bin`). ## Loading ```python from huggingface_hub import snapshot_download root = snapshot_download(repo_id="anonymous-user-submission/TextureBench-3DGS", repo_type="dataset") ``` ## Privacy and licence All images are original captures of public outdoor spaces; no people are the subject of any view and EXIF metadata (device, location) has been stripped. Released under CC BY-NC 4.0 for research use.