| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-to-3d |
| - depth-estimation |
| language: |
| - en |
| tags: |
| - 3d-reconstruction |
| - dynamic-scenes |
| - gaussian-splatting |
| - novel-view-synthesis |
| - 360-degree |
| pretty_name: iPhone360 |
| --- |
| |
| # iPhone360 Dataset (Simple Version) |
|
|
| iPhone360 is a benchmark dataset for 360° reconstruction of dynamic objects from monocular video, introduced in the paper: |
|
|
| > **4DGS360: 360° Gaussian Reconstruction of Dynamic Objects from a Single Video** |
| > Jae Won Jang, Yeonjin Chang, Wonsik Shin, Juhwan Cho, Nojun Kwak |
| > [Project Page](https://jaewon040.github.io/4dgs360/) · [arXiv](https://arxiv.org/abs/2603.21618) |
|
|
| ## Dataset Description |
|
|
| iPhone360 features real-world dynamic scenes captured with an iPhone, where **test cameras are positioned at significantly different angles from training views**. This enables evaluation of 360° reconstruction capabilities that existing datasets cannot provide. |
|
|
| ## Dataset Versions |
|
|
| This dataset is distributed in two versions: |
|
|
| - **`iPhone360 simple_version`** (this folder) — RGB/depth/mask/camera/points/splits data plus lightweight VDA depth and lidar alignment, with the heavy 4DGS360-specific intermediate preprocessing outputs (2D/3D tracks, track-anything masks, AnchorTAPIP3D refined depth/tracks, cached scene-normalization tensors) excluded. Much smaller download. |
| - **[`iPhone360-4dgs360 preprocessed version`](https://huggingface.co/datasets/mipal/iPhone360-4dgs360)** — includes *all* preprocessing outputs required to reproduce 4DGS360 training and evaluation end-to-end. Large footprint. |
| |
| **If you're quickly adapting iPhone360 to a new paper/method, we recommend starting here with `simple_version`** and evaluating on it first, rather than downloading the full `preprocessed_version`. Only fall back to `preprocessed_version` if you specifically need to reproduce 4DGS360's own training pipeline. |
|
|
| ## Scenes |
|
|
| | Scene | Description | |
| |-------|-------------| |
| | `block2` | Dynamic object scene | |
| | `goat` | Dynamic object scene | |
| | `jacket` | Dynamic object scene | |
| | `jelly` | Dynamic object scene | |
| | `pull-up` | Dynamic object scene | |
| | `walk-around` | Dynamic object scene | |
|
|
| ## Data Structure |
|
|
| Each scene contains: |
| - `rgb/` — RGB frames |
| - `depth/` — Depth maps |
| - `masks/` — Object masks |
| - `camera/` — Camera parameters |
| - `splits/` — Train/test split definitions |
| - `points.npy` — Initial point cloud |
| - `dataset.json` / `scene.json` / `metadata.json` — Scene metadata |
| - `flow3d_preprocessed/` — Lightweight preprocessed data (video depth, lidar-aligned depth); does **not** include the 4DGS360-specific tracks/cache/refined-depth outputs found in `preprocessed_version` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @article{jang2025_4dgs360, |
| title = {4DGS360: 360° Gaussian Reconstruction of Dynamic Objects from a Single Video}, |
| author = {Jang, Jae Won and Chang, Yeonjin and Shin, Wonsik and Cho, Juhwan and Kwak, Nojun}, |
| journal = {arXiv preprint arXiv:2603.21618}, |
| year = {2025}, |
| url = {https://arxiv.org/abs/2603.21618} |
| } |
| ``` |
|
|
