mipal/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 · arXiv

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

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:

@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}
}