| --- |
| pretty_name: DL3DV-OVS |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - 3d |
| - open-vocabulary |
| - gaussian-splatting |
| - object-segmentation |
| - evaluation |
| gated: true |
| extra_gated_heading: "Access DL3DV-OVS" |
| extra_gated_description: >- |
| Confirm the conditions below to access the files. You must separately accept |
| the official DL3DV-10K terms for both source repositories. |
| extra_gated_button_content: "Agree and access" |
| extra_gated_fields: |
| I have accepted the official DL3DV-10K Terms of Use: checkbox |
| I will use these files only for non-commercial research or education: checkbox |
| I will not redistribute DL3DV source content: checkbox |
| --- |
| |
| # DL3DV-OVS: Open-Vocabulary 3D Scene Understanding Dataset for Large, Complex Indoor–Outdoor Scenes |
|
|
| DL3DV-OVS is a four-scene dataset and evaluation benchmark for open-vocabulary |
| 3D scene understanding in large, complex indoor and outdoor environments. It |
| was introduced with **LightSplat**. |
|
|
| ## Dataset contents |
|
|
| | Component | Park | Shop | Road | Office | Total | Source | |
| | --- | ---: | ---: | ---: | ---: | ---: | --- | |
| | RGB/COLMAP frames | 314 | 407 | 317 | 378 | 1,416 | DL3DV | |
| | Annotated frames | 5 | 3 | 3 | 3 | 14 | Ours | |
| | GT masks | 12 | 17 | 18 | 11 | 58 | Ours | |
| | SAM/CLIP pairs | 314 | 407 | 317 | 378 | 1,416 | Ours | |
| | Text embeddings | 5 | 8 | 7 | 5 | 22 unique | Ours | |
| | RGB 3DGS checkpoints | 1 | 1 | 1 | 1 | 4 | Ours | |
|
|
| The 58 masks provide ground truth for 14 evaluation frames; the 1,416 |
| SAM/OpenCLIP pairs cover all scene frames. |
|
|
| Original DL3DV RGB frames, cameras, and COLMAP caches are not included. Obtain |
| them from the official DL3DV repositories after accepting their terms. |
| Source images are 960×540. The companion setup applies COLMAP undistortion, and |
| the provided features and masks match its output. |
|
|
| ## Access requirements |
|
|
| Before accessing these files, request access to both official DL3DV source |
| repositories: |
|
|
| 1. [DL3DV/DL3DV-ALL-960P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-960P) |
| 2. [DL3DV/DL3DV-ALL-ColmapCache](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-ColmapCache) |
|
|
| The DL3DV maintainers state that requesting access accepts the |
| [DL3DV-10K Terms of Use](https://github.com/DL3DV-10K/Dataset/blob/main/License.md). |
| Access here does not replace that upstream agreement. |
|
|
| ## Files |
|
|
| ```text |
| label/<scene>/gt/<frame>/<query>.jpg |
| reference/<scene>/language_features/<frame>_s.npy |
| reference/<scene>/language_features/<frame>_f.npy |
| reference/<scene>/checkpoint/chkpnt30000.pth |
| reference/text/text_features.json |
| metadata/annotations.json |
| metadata/scenes.json |
| ``` |
|
|
| Public scene names are `park`, `shop`, `road`, and `office`. The masks follow |
| the LERF-OVS evaluation layout and use 8-bit grayscale JPEG, with foreground |
| defined as `pixel > 10`. Each `_s.npy` stores per-pixel SAM mask IDs and the |
| matching `_f.npy` stores one OpenCLIP image embedding per mask. The shared JSON |
| contains normalized OpenCLIP text embeddings for all 22 benchmark queries. |
| The encoder is OpenCLIP ViT-B-16 (`laion2b_s34b_b88k`). |
|
|
| ## Setup |
|
|
| Use the companion [DL3DV-OVS repository](https://github.com/vision3d-lab/DL3DV-OVS) |
| to assemble the complete benchmark: |
|
|
| ```bash |
| dl3dv-ovs setup data/dl3dv-ovs |
| ``` |
|
|
| ## License |
|
|
| DL3DV-OVS masks, features, checkpoints, and benchmark metadata are provided |
| under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/), subject |
| to the [DL3DV-10K Terms of Use](https://github.com/DL3DV-10K/Dataset/blob/main/License.md). |
| Original DL3DV data is not redistributed. See [`LICENSE.md`](LICENSE.md). |
|
|
| ## Citation |
|
|
| If you use DL3DV-OVS, cite both LightSplat and DL3DV-10K. |
|
|
| ```bibtex |
| @inproceedings{bang2026lightsplat, |
| title={Lightsplat: Fast and memory-efficient open-vocabulary 3d scene understanding in five seconds}, |
| author={Bang, Jaehun and Kim, Jinhyeok and Kim, Minji and Jeong, Seungheon and Joo, Kyungdon}, |
| booktitle={2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| pages={19812--19821}, |
| year={2026}, |
| organization={IEEE} |
| } |
| |
| @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={2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| pages={22160--22169}, |
| year={2024}, |
| organization={IEEE} |
| } |
| ``` |
|
|
