| --- |
| license: apache-2.0 |
| library_name: pytorch |
| base_model: depth-anything/DA3-BASE |
| base_model_relation: finetune |
| tags: |
| - arxiv:2608.29733 |
| - computer-vision |
| - image-pair-classification |
| - structure-from-motion |
| - visual-disambiguation |
| - doppelgangers |
| --- |
| |
| # XDG: Accelerated Visual Disambiguation |
|
|
| XDG scores image pairs to detect visually similar but geometrically inconsistent |
| matches before Structure from Motion (SfM). Filtering these pairs helps prevent |
| incorrect connections and corrupted reconstructions. |
|
|
| [Paper](https://arxiv.org/abs/2608.29733) · |
| [Code](https://github.com/xtcpete/xdg) · |
| [Project page](https://xtcpete.github.io/xdg/) |
|
|
| ## Model details |
|
|
| The released model uses a Depth Anything 3 base backbone with rank-8 LoRA, |
| camera-token features from four stages, and a binary classifier. It aggregates |
| features from both image orders. The default inference configuration resizes |
| RGB images to 560 × 560 pixels. |
|
|
| `xdg.pth` contains the complete PyTorch state dictionary, including the backbone, |
| LoRA weights, and classifier (121,403,650 parameters). A separate DA3 weight |
| download is not needed at inference time. The architecture implementation is |
| provided by the linked code repository, including its vendored DA3 dependency. |
| This is a custom PyTorch model; use the XDG inference code below. |
|
|
| | File | Contents | |
| | --- | --- | |
| | `xdg.pth` | Released checkpoint | |
| | `xdg.yaml` | Matching architecture and inference configuration | |
| | `LICENSE` | Apache 2.0 license for XDG code and checkpoint | |
| | `LICENSE-Depth-Anything-3` | Vendored Depth Anything 3 license | |
|
|
| ## Usage |
|
|
| Install the code and dependencies: |
|
|
| ```bash |
| git clone https://github.com/xtcpete/xdg.git |
| cd xdg |
| conda env create -f environment.yml |
| conda activate xdg |
| ``` |
|
|
| Download the checkpoint and its matching configuration: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| for filename in ("xdg.pth", "xdg.yaml"): |
| hf_hub_download("xtcpete/xdg", filename, local_dir="weights") |
| ``` |
|
|
| Create `pairs.txt` with two whitespace-separated image paths per line, relative |
| to your image directory: |
|
|
| ```text |
| image_0001.jpg image_0002.jpg |
| subdir/image_0003.jpg subdir/image_0004.jpg |
| ``` |
|
|
| Score the pairs from the root of the code checkout: |
|
|
| ```bash |
| python remove_doppelgangers.py \ |
| --config weights/xdg.yaml \ |
| --ckpt weights/xdg.pth \ |
| --pairs_txt pairs.txt \ |
| --input_image_path path/to/images \ |
| --output_path output/pairs \ |
| --batch_size 1 |
| ``` |
|
|
| CUDA is the default device; add `--device cpu` for CPU execution. Start with a |
| batch size of 1 and increase it to suit available memory. Use a fresh output |
| directory for each run: existing probability files are reused by the script. |
|
|
| The output `output/pairs/pair_probability_list.npy` contains a dictionary whose |
| `prob` array has shape `(number_of_pairs, 2)`, in input-pair order. Column 1 is |
| the score for a valid (non-doppelganger) pair. The database filter removes pairs |
| whose column-1 score is below the chosen threshold (default: 0.8). |
|
|
| To filter an existing COLMAP database, replace `--pairs_txt pairs.txt` with |
| `--database_path path/to/database.db` and optionally set `--threshold 0.8`. |
| The database must contain geometrically verified pairs in `two_view_geometries`. |
| The script creates a filtered copy and preserves the input database. |
|
|
| ## Training and evaluation |
|
|
| The released training configuration references Doppelgangers training pairs, |
| MegaDepth-derived pairs, and VisymScenes training pairs, following the dataset |
| setup used by [Doppelgangers++](https://github.com/doppelgangers25/doppelgangers-plusplus). |
| See the code repository's |
| [training configuration](https://github.com/xtcpete/xdg/blob/main/configs/training_configs/training.yaml) |
| for hyperparameters and data layout. |
|
|
| The project reports comparable disambiguation performance to DG++ with 3.5× |
| faster inference across its pairwise and SfM benchmarks. See the paper for the |
| benchmark conditions and detailed results. Use `test.py` in the code repository |
| to evaluate the checkpoint on the Doppelgangers and VisymScenes test sets. |
|
|
| ## Intended use and limitations |
|
|
| XDG is intended for image-pair disambiguation and filtering matches before SfM. |
| Its scores are learned predictions, not a guarantee of geometric consistency. |
| False positives can remove useful matches, and false negatives can leave |
| incorrect connections. Choose the threshold for the reconstruction and dataset; |
| the published results do not guarantee the same performance on other imagery. |
| The checkpoint does not include feature matching, geometric verification, or |
| COLMAP reconstruction itself. |
|
|
| ## License |
|
|
| The original XDG code and checkpoint are released under Apache 2.0. Depth |
| Anything 3 retains its own included license. Training and evaluation datasets |
| remain subject to their providers' licenses and terms. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{chen2026xdgacceleratedvisualdisambiguation, |
| title={XDG: Accelerated Visual Disambiguation}, |
| author={Gonglin Chen and Ben Southall and Hanyuan Xiao and Wenbin Teng and Haolin Xiong and Tianwen Fu and Junyi Ouyang and Kshitij Singh Minhas and Supun Samarasekera and Rakesh Kumar and Yajie Zhao}, |
| year={2026}, |
| eprint={2608.29733}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2608.29733}, |
| } |
| ``` |
|
|
