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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}
}
```
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