Note

This is a Hugging Face dataset. For large datasets, ensure huggingface_hub>=1.1.3 to avoid rate limits. Learn more in the Hugging Face integration docs.

Dataset Card for WideDepth in FiftyOne#

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FiftyOne dataset for WideDepth — an indoor fisheye depth-estimation benchmark (ICRA 2026) with millimeter-accurate ground-truth depth and disparity rendered from high-resolution LiDAR scans.

We use one fixed camera configuration from the full WideDepth benchmark — 195° FOV, 300 mm focal length, CENTER stereo position — across all 101 indoor scenes.

The full Hub release has many combinations (4 FOVs × 5 focal lengths × 3 positions, plus multiple RGB/depth/disparity views per config). We standardize on the widest FOV setting highlighted in the paper’s hardest evaluations, so every scene shares the same virtual rig instead of mixing 60+ configs per scene.

Installation#

If you haven’t already, install FiftyOne:

pip install -U fiftyone

Usage#

import fiftyone as fo
from huggingface_hub import snapshot_download


# Download the dataset snapshot to the current working directory

snapshot_download(
    repo_id="Voxel51/widedepth", 
    local_dir=".", 
    repo_type="dataset"
    )

# Load dataset from current directory using FiftyOne's native format
dataset = fo.Dataset.from_dir(
    dataset_dir=".",  # Current directory contains the dataset files
    dataset_type=fo.types.FiftyOneDataset,  # Specify FiftyOne dataset format
    name="WideDepth"  # Assign a name to the dataset for identification
)

# Launch the App
session = fo.launch_app(dataset)

Dataset Soureces#

Project page: https://ilyaind.github.io/WideDepth/
Hugging Face: https://huggingface.co/datasets/IlyaInd/WideDepth
Paper: https://arxiv.org/pdf/2605.24074v1
Video overview: https://www.youtube.com/watch?v=zmxO8p5tIB8

About WideDepth#

WideDepth provides synthetic stereo RGB, dense depth, and disparity for 101 indoor scenes across multiple camera configurations. This FiftyOne dataset uses a single fixed rig per scene:

Parameter	Value
FOV	195° (widest setting in the benchmark)
Focal length	300 mm
Stereo position	CENTER

Known gaps: Scenes 096_092_000 and 097_092_270 are missing the pano_crop view and have no 3D slice.

Dataset summary#

Property	Value
Name	`widedepth_195fov_300mm_center`
Type	Grouped (multimodal)
Groups	101 (one per scene)
Total samples	398
Default slice	`pano_crop`

Group structure#

Each group is one scene (e.g. 001_057_000) with up to four slices:

Slice	Media type	Description
`fisheye`	image	Fisheye RGB + depth
`pano`	image	Equirectangular RGB + depth + disparity
`pano_crop`	image	Cropped equirectangular RGB + depth + disparity
`pointcloud`	3d	Colored 3D point cloud (`fo3d`) from the `pano_crop` view

WideDepth ships RGB, depth, and disparity only — not ready-made point clouds. For this dataset, we backprojected the pano_crop ground-truth depth (metric, millimeter-accurate) into 3D using the paper’s equirectangular camera model, colored each point from the matching RGB pixel, and packaged the result as an fo3d scene for the FiftyOne 3D viewer. Each cloud is a single-view snapshot from one capture position, not a full room reconstruction.

Group: scene_id = "001_057_000"
 fisheye      →  image
 pano         →  image
 pano_crop    →  image  (default)
 pointcloud   →  3d

Switch slices in the App to compare projections or open the 3D view for the same capture.

Sample fields#

Field	Type	Description
`filepath`	`str`	Path to the RGB image or `fo3d` scene file
`group`	`fo.Group`	Slice identifier within the scene group
`scene_id`	`str`	Scene name, e.g. `001_057_000`
`fov`	`str`	`195FOV`
`focal_mm`	`str`	`300mm`
`position`	`str`	`CENTER`
`view`	`str`	`fisheye`, `pano`, `pano_crop`, or `pointcloud`

Labels#

Depth and disparity are stored as fo.Heatmap labels on the image slices (not as separate samples).

Field	Type	On slices
`depth`	`fo.Heatmap`	`fisheye`, `pano`, `pano_crop`
`disparity`	`fo.Heatmap`	`pano`, `pano_crop`

Depth and disparity maps are 16-bit PNG ground truth in millimeters. Zero values indicate invalid or masked regions (common on wide panoramic views).

Citation#

@article{indyk2026widedepth,
  title   = {WideDepth: Millimeter-Accurate Benchmark for Fisheye Depth Estimation},
  author  = {Indyk, Ilia and Penshin, Ignat and Sosin, Ivan and Monastyrny, Maxim and Valenkov, Aleksei and Makarov, Ilya},
  journal = {arXiv preprint arXiv:2605.24074},
  year    = {2026},
  url     = {https://arxiv.org/abs/2605.24074}
}