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 — 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:

@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}
}