Dataset Card for cleardepth

ClearDepth Transparent (FiftyOne) is a grouped FiftyOne dataset built from the synthetic transparent-object stereo data described in the ClearDepth paper (SynClearDepth).

Each group represents one indoor scene with stereo RGB video, dense per-pixel labels, and (optionally) a merged 3D point-cloud reconstruction of the same 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/ClearDepth", 
    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="ClearDepth"  # Assign a name to the dataset for identification
)

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

Dataset Details

Dataset Description

This is a FiftyOne dataset with 204 samples.

Scenes are synthetic renders of household rooms containing transparent objects, with left/right stereo views, ground-truth depth, surface normals, instance segmentation, and camera poses.

• Source paper: ClearDepth: Enhanced Stereo Perception of Transparent Objects for Robotic Manipulation (arXiv:2409.08926)
• Project page: https://sites.google.com/view/cleardepth

What is in this release

This FiftyOne build contains 204 scenes drawn from the local transparent_dataset export. Frame count varies by scene (commonly 51–121 frames). All RGB frames are 1280×720.

Room categories present in the scene names:

• bathroom
• diningroom
• kitchen
• livingroom

Each scene name follows {room}{id}_{variant}_circle{circle_id}, for example bathroom001_0000_circle000.

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FiftyOne Dataset Structure

Media type: group

Default group slice: left

Groups and slices

There are 204 groups. Each group links one or more slices of the same scene:

Slice	Media type	Description
left	video	Left stereo camera as an H.264 MP4
right	video	Right stereo camera as an H.264 MP4
reconstruction	3d	Merged RGB-colored point cloud (scene.fo3d), if reconstruction has been run

Switch slices in the FiftyOne App to view the left video, right video, or 3D reconstruction for the same scene. The left and right slices carry identical sample metadata and matching frame-aligned labels for their respective eyes.

Sample-level fields

Present on the video slices (left, right) and partially mirrored on reconstruction:

Field	Type	Description
scene_name	string	Unique scene id, e.g. bathroom001_0000_circle000
room_type	string	Room category (bathroom, diningroom, kitchen, livingroom)
room_id	string	Numeric room id within the category
room_name	string	Combined room label, e.g. bathroom001
scene_variant	string	Scene variant index from the source export
circle_id	string	Camera trajectory / circle index
scene_objects	list	Objects in the scene; each entry has object_id, category, name, and transform
object_categories	list	Sorted unique object categories in the scene
tags	list	["reconstruction"] on the reconstruction slice only

The reconstruction slice repeats the scene metadata fields above but has no frame labels.

Frame-level fields (video slices only)

Labels are attached to frames on left and right only:

Field	FiftyOne type	Description
depth	Heatmap	Metric depth stored as a 16-bit PNG (millimeters on disk). App heatmap range defaults to 0–3000 mm. Decode to meters with depth_m = png_mm / 1000.
normal	Heatmap	RGB-encoded surface normal PNG. Directions use the standard mapping pixel = 127.5 * (normal + 1) with unit-length normals.
segmentation	Segmentation	Per-pixel instance segmentation mask PNG
camera_pose	list	4×4 camera-to-world pose matrix for that frame

Depth and normal fields reference PNG files on disk via absolute map_path values inside the Heatmap / Segmentation documents.

There are no bounding-box or classification labels. Supervision is dense per-pixel depth, normals, segmentation, plus per-frame camera pose and scene-level object metadata.

---

3D Reconstruction Slice

When reconstruction has been added, each group includes a reconstruction slice pointing at a FiftyOne scene.fo3d file that wraps one merged RGB-colored point cloud for the whole scene (not one cloud per frame).

The cloud is built by back-projecting depth from the left camera across all frames, coloring points from the corresponding RGB images, and merging them into a single scene-level point cloud. It is intended for interactive 3D viewing in FiftyOne, not watertight mesh reconstruction.

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Direct Use

Suitable for:

• Exploring stereo transparent-object scenes in FiftyOne
• Training or evaluating dense depth / normal / segmentation models
• Inspecting camera motion and scene object layout
• Comparing left vs. right stereo views and 3D reconstructions side by side

Out-of-Scope Use

• Real-world deployment without adaptation: source data is synthetic
• Object detection benchmarks: only dense pixel labels and scene object lists are provided
• Metric grasp planning from reconstruction alone: the 3D slice is a merged point cloud for visualization, not a calibrated manipulation pipeline

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Citation

If you use the source ClearDepth dataset or method, cite:

@article{bai2024cleardepth,

  title={ClearDepth: Enhanced Stereo Perception of Transparent Objects for Robotic Manipulation},

  author={Bai, Kaixin and Zeng, Huajian and Zhang, Lei and Liu, Yiwen and Xu, Hongli and Chen, Zhaopeng and Zhang, Jianwei},

  journal={arXiv preprint arXiv:2409.08926},

  year={2024}

}



@article{bai2025stereoanything,

  title={StereoAnything: Advanced Zero-Shot Stereo Imaging for Multi-Finger Grasp Detection with Transparent Objects},

  author={Bai, Kaixin and Zhang, Lei and Liu, Yiwen and Chen, Zhaopeng and Zhang, Jianwei},

  journal={Authorea Preprints},

  year={2025},

  doi={10.36227/techrxiv.174612328.83478240/v1},

url={https://doi.org/10.36227/techrxiv.174612328.83478240/v1},

  publisher={Authorea}

}