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--- |
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license: other |
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pretty_name: "D-RE10K: Dynamic Real-Estate 10K" |
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language: |
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- en |
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tags: |
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- video |
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- computer-vision |
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- novel-view-synthesis |
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- 3d-reconstruction |
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- dynamic-scenes |
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- research |
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- gated |
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extra_gated_heading: "Request access to D-RE10K (Research-Only)" |
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extra_gated_description: > |
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D-RE10K contains processed real-estate walkthrough video clips derived from third-party sources. |
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Access is granted for non-commercial research only. |
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We do not grant rights to any underlying third-party content. You are responsible for ensuring you have the necessary rights to use the media. |
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By requesting access, you agree to use this dataset for non-commercial research purposes only. |
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extra_gated_button_content: "Agree & Request Access" |
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--- |
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# D-RE10K: Dynamic Real-Estate 10K Dataset |
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## Overview |
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This dataset contains the **DRE10K training** split (15,467 clips, 147,422 frames) and the **DRE10K mask** test split (76 clips, 1,541 frames), released on Hugging Face for research on self-supervised large view synthesis in dynamic environments. The data is collected from real-estate walkthrough videos and curated specifically for training and evaluating novel view synthesis models in scenes with dynamic objects. |
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Our dataset builds on the Real-Estate 10K collection and extends it with per-frame binary masks, masked videos, COLMAP reconstructions, and DPVO camera trajectories for the test split. Each clip is accompanied by JSON metadata containing camera intrinsics and world-to-camera poses, making it a versatile resource for tasks such as novel view synthesis, camera pose estimation, and dynamic scene understanding. |
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For more details, please refer to our paper [WildRayZer: Self-supervised Large View Synthesis in Dynamic Environments](https://arxiv.org/abs/2601.10716). |
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| Split | Clips | Extracted Frames | Metadata (JSON) | Binary Masks | Masked Videos | COLMAP | DPVO | |
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|-------|------:|------------------:|------------------:|--------------:|--------------:|-------:|-----:| |
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| Train | 15,467 | 147,422 | 15,467 | — | — | — | — | |
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| Test | 76 | 1,541 | 76 | 1,540 | 76 | 76 scenes | 76 | |
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## Key Features |
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- **Size**: 15,467 training clips with 147,422 extracted frames; 76 test clips with 1,541 frames. |
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- **Representation**: Extracted PNG frames from real-estate walkthrough videos, with per-clip JSON metadata (camera intrinsics, world-to-camera poses, frame paths). |
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- **Train split** includes: |
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- Video clips (`.mp4`) |
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- Extracted frames (`.png`) |
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- Per-clip JSON metadata |
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- **Test split** additionally includes: |
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- Per-frame binary masks (`.png`) for dynamic objects |
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- Masked videos with dynamic objects removed (`.mp4`) |
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- COLMAP reconstructions (sparse models in binary & text, masks, database) |
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- DPVO estimated camera trajectories (`.txt`) |
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## Dataset Format |
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The dataset is provided in a format ready for view-synthesis and 3D-reconstruction research: |
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- **Videos**: Stored as `.mp4` files under `videos/`. |
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- **Frames**: Stored as `.png` files under `images/<clip_id>/`. |
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- **Metadata**: Stored as `.json` files under `metadata/`. Each JSON file contains camera intrinsics (`fxfycxcy`), 4×4 world-to-camera matrices (`w2c`), and frame paths. |
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- **Binary Masks** (test only): Stored as `.png` files under `binary_masks/<clip_id>/`. |
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- **COLMAP** (test only): Full sparse reconstructions under `colmap/<clip_id>/` (includes `sparse/`, `masks/`, `database.db`). |
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- **DPVO** (test only): Camera trajectory files under `dpvo/<clip_id>.txt`. |
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The dataset is distributed as multi-part zip archives. After downloading, unzip them as follows: |
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```bash |
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# Unzip training data (8 parts) |
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mkdir -p train |
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for f in train_zip/train_*.zip; do |
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unzip -o "$f" -d ./train |
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done |
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# Unzip test data (3 parts) |
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mkdir -p test |
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for f in test_zip/test_*.zip; do |
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unzip -o "$f" -d ./test |
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done |
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``` |
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After unzipping, you should see the `train/` and `test/` directories with the structure described above. |
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## License |
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This dataset is released for **non-commercial research use only**. |
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The video clips and frames are derived from third-party sources. We do not hold the copyright to the underlying audio-visual content. |
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Users must agree to the terms outlined in the [LICENSE](LICENSE.md) file, which include: |
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- Use for non-commercial research only. |
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- No redistribution of the dataset. |
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- Acknowledgment of third-party rights. |
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## Takedown Policy |
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The video clips in this dataset are derived from third-party sources. If any clips need to be taken down (e.g., due to privacy concerns or copyright requests), we will promptly delete them from this dataset. Please contact us at `xuweic@virginia.edu` for such requests. |
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## Citation |
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If you find this dataset useful in your research, please cite our work: |
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```bibtex |
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@article{chen2026wildrayzerselfsupervisedlargeview, |
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title={WildRayZer: Self-supervised Large View Synthesis in Dynamic Environments}, |
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author={Xuweiyi Chen and Wentao Zhou and Zezhou Cheng}, |
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year={2026}, |
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eprint={2601.10716}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2601.10716}, |
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} |
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``` |
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