--- pretty_name: DL3DV-10K-Meshed license: cc-by-nc-4.0 gated: true size_categories: - 10K This is a derivative of DL3DV-10K. The underlying data remains subject to the DL3DV-10K Terms of Use and CC BY-NC 4.0. You must accept those terms in full before downloading. extra_gated_prompt: > By requesting access you agree to the DL3DV-10K Terms of Use (https://github.com/DL3DV-10K/Dataset/blob/main/License.md), in particular: you will use the data only for non-commercial research and educational purposes; you accept full responsibility for your use of it; you will not redistribute it except to individuals who have themselves accepted these terms; and you will be able to delete person-specific data on request. If you are employed by a for-profit entity, your employer is also bound by these terms and you confirm you are authorised to accept on its behalf. extra_gated_fields: Full name: text Institution or company: text Country: country Intended use: type: select options: - Academic research - Education - label: Other value: other I have read and accept the DL3DV-10K Terms of Use in full: checkbox I will use this dataset for non-commercial purposes ONLY: checkbox I will not redistribute this data to anyone who has not accepted these terms: checkbox extra_gated_button_content: "Accept terms and request access" --- # DL3DV-10K-Meshed A derivative of [DL3DV-10K](https://huggingface.co/DL3DV) providing undistorted 480P views together with ground-truth surface geometry, used to train [Surflo](https://anttwo.github.io/surflo/). **This dataset is not a replacement for DL3DV-10K.** It redistributes some of DL3DV-10K imagery, and remains subject to the DL3DV-10K Terms of Use. See [Licensing and terms](#licensing-and-terms) before using it. ## What we changed Relative to [DL3DV-ALL-480P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P): - **Undistorted images.** All 480P frames were reprocessed with COLMAP to remove lens distortion. Cameras are correspondingly pinhole. - **Reference surface meshes.** A full-scene mesh was reconstructed for every scene with [Gaussian Wrapping](https://diego1401.github.io/BlobsToSpokesWebsite/) (hence the `gw_output/` directory name). - **Per-view depth.** Depth maps of the reconstructed mesh, rendered from each camera. - **Oriented surface samples.** 10M points with normals sampled on the mesh surface, chunked across several files per scene. Not included: 2K/4K frames, source videos. Scene hashes match DL3DV-10K exactly, so a scene here can always be traced back to its upstream counterpart. ## Repository layout Scenes are packed one archive per scene, grouped into subsets mirroring DL3DV-10K: ``` 1K/.tar 2K/.tar ... 10K/.tar 11K/.tar ``` ### Input scene layout Each scene is a **COLMAP-style dataset** (posed RGB views) augmented with **pseudo-GT surface labels**, packed as `.tar` with the internal layout: ``` // ├── images/ # RGB views (jpg/png; extension auto-detected) │ └── . ├── sparse/ # COLMAP output │ └── ... └── gw_output/ ├── cameras.json # per-view cameras (3DGS / COLMAP format) ├── scene_extent.pth # dict: {"scene_radius": float, "scene_center": (3,)} ├── point_cloud/ │ └── iteration_30000/ │ └── point_cloud.ply # Gaussians optimized with Gaussian Wrapping ├── mesh_<...>.ply # Mesh representing the full scene, obtained with Gaussian Wrapping ├── depth/ │ └── .pth # per-view mesh-depth tensor (H, W), stem matches image ├── surface_point_labels/ │ └── *.pth # mesh surface points — chunked (N, 3) tensors └── surface_normal_labels/ └── *.pth # mesh surface normals — chunked (N, 3) tensors ``` Each `cameras.json` entry holds `img_name` (stem, no extension), `width`, `height`, `position` (3,), `rotation` (3×3), `fx` and `fy`. The `surface_point_labels` / `surface_normal_labels` are oriented points sampled from the scene's **ground-truth mesh**. We obtained the meshes by running **Gaussian Wrapping** on each COLMAP scene (hence the `gw_output/` name). Point and normal chunks are index-aligned: the *i*-th row of a normal chunk is the normal at the *i*-th row of the correspondingly named point chunk. Concatenating all chunks in sorted filename order yields the full 10M-point set. ## Getting access This dataset is gated. You must accept the DL3DV-10K Terms of Use before downloading; approval is automatic once you do. You will then need to be authenticated: ```bash hf auth login ``` ## Download The full dataset is several terabytes. Full download: ```python from huggingface_hub import snapshot_download snapshot_download( repo_id="AntoineGuedon/DL3DV-10K-Meshed", repo_type="dataset", local_dir="dl3dv-meshed", ) ``` Download one subset at a time: ```python from huggingface_hub import snapshot_download snapshot_download( repo_id="AntoineGuedon/DL3DV-10K-Meshed", repo_type="dataset", allow_patterns="1K/*.tar", local_dir="dl3dv-meshed", ) ``` A single scene, by hash: ```python from huggingface_hub import hf_hub_download hf_hub_download( repo_id="AntoineGuedon/DL3DV-10K-Meshed", repo_type="dataset", filename="1K/.tar", local_dir="dl3dv-meshed", ) ``` ## Licensing and terms The image data originates from DL3DV-10K and remains subject to the [DL3DV-10K Terms of Use](https://github.com/DL3DV-10K/Dataset/blob/main/License.md) and CC BY-NC 4.0. This release is a derivative; We are not relicensing the original data, and nothing here grants rights beyond those in the original license. Our contributions — the undistorted reprocessing, rendered depth maps, and sampled surface points and normals — are released under CC BY-NC 4.0. In particular, you may use this dataset for **non-commercial research and educational purposes only**, you must not redistribute it to anyone who has not accepted the DL3DV-10K Terms of Use, and you must be able to delete person-specific data on request. ## Citation If you use this dataset, please cite **both** DL3DV-10K and Surflo. If you use the surface labels, please also cite the mesh reconstruction method. ```bibtex @inproceedings{ling2024dl3dv, title={DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision}, author={Ling, Lu and Sheng, Yichen and Tu, Zhi and Zhao, Wentian and Xin, Cheng and Wan, Kun and Yu, Lantao and Guo, Qianyu and Yu, Zixun and Lu, Yawen and others}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={22160--22169}, year={2024} } @article{guedon2026surflo, title={Surflo: Consistent 3D Surface Flow from a Global State}, author={Gu\'edon, Antoine and Nakamura, Shu and Dufour, Nicolas and Lei, Jiahui and Nishino, Ko and Kanazawa, Angjoo}, journal={arXiv preprint arXiv:2606.13644}, year={2026} } @article{gomez2026blobsspokes, title={From Blobs to Spokes: High-Fidelity Surface Reconstruction via Oriented Gaussians}, author={Gomez, Diego and Gu\'edon, Antoine and Maruani, Nissim and Gong, Bingchen and Ovsjanikov, Maks}, journal={ECCV}, year={2026} } ``` ## Acknowledgements Our thanks to Lu Ling and the DL3DV-10K team for building and sharing the original dataset.