adfa5456/Final_Dataset

Transparent Object Point Cloud Dataset

Project Page | Paper | Code | Dataset

Dataset Description

This dataset contains multi-view RGB images, raw and processed point clouds, object-level reference point clouds, and 6D pose annotations for transparent-object pose estimation and point-cloud registration.

The dataset is organized into two subsets:

• Printed objects: 6 object instances (Eyeglass, Kettle, Lightbulb, Lighter, Magnifying_glass, and Spray).
• Scanned objects: 9 object instances (bulb_0, bulb_1, cctv_0, cctv_1, cup_0, jar_0, jar_1, pet_0, and pet_1).

The current repository contains approximately 1,500 views in total. Most objects have about 100 views. The data occupies approximately 73 GB when stored uncompressed on disk.

Intended Uses

The dataset may be useful for research on:

• Transparent-object 6D pose estimation
• Point-cloud registration and ICP
• Depth or point-cloud completion
• RGB/point-cloud multimodal reconstruction
• Robust pose estimation under missing or noisy depth measurements

This dataset has not been validated for safety-critical, medical, or production robotic applications.

Dataset Structure

.
├── dataset_printed/
│   ├── Image/<object>/<view>.png
│   ├── raw/<object>/<view>.ply
│   ├── processed/<object>/noisy_filtered_<view>.ply
│   ├── gt/<object>/noisy_filtered_<view>.json
│   └── gt_pcd/<object>/gt_filtered.ply
└── dataset_scanned/
    ├── Image/<object>/<view>.png
    ├── raw/<object>/<view>.ply
    ├── processed/<object>/noisy_filtered_<view>.ply
    ├── gt/<object>/result_<view>.txt
    ├── gt_pcd/<object>/gt_filtered.ply
    └── vggt/<object>/
        ├── <view>.ply
        └── <view>_conf.txt

There are no predefined train, validation, or test splits. Users should define object-wise or view-wise splits appropriate for their experiments and report them with their results.

File Types

Path	Format	Description
Image/	PNG	RGB image associated with each view. Images are 1932 x 1096 pixels in the current release.
raw/	PLY	Raw point cloud captured for a view.
processed/	PLY	Filtered/processed version of the corresponding raw point cloud.
gt/	JSON or TXT	Ground-truth 6D pose. Printed objects use JSON; scanned objects use a 4 x 4 matrix stored as text.
gt_pcd/	PLY	Reference point cloud for each object instance.

View Naming Convention

Each sample follows the naming convention:

<fill_rate>_<sample_index>

Loading the Dataset

This repository stores the original files directly and does not currently provide a Hugging Face datasets loading script. Download a snapshot while preserving the directory structure:

from huggingface_hub import snapshot_download

dataset_dir = snapshot_download(
    repo_id="adfa5456/Final_Dataset",
    repo_type="dataset",
)
print(dataset_dir)

Example: load an RGB image, a point cloud, and a scanned-object pose:

from pathlib import Path

import numpy as np
import open3d as o3d
from PIL import Image

root = Path(dataset_dir)
object_name = "pet_1"
view = "0_1"

image = Image.open(root / "dataset_scanned" / "Image" / object_name / f"{view}.png")
raw_pcd = o3d.io.read_point_cloud(
    str(root / "dataset_scanned" / "raw" / object_name / f"{view}.ply")
)
processed_pcd = o3d.io.read_point_cloud(
    str(root / "dataset_scanned" / "processed" / object_name / f"noisy_filtered_{view}.ply")
)
pose = np.loadtxt(root / "dataset_scanned" / "gt" / object_name / f"result_{view}.txt")

print(image.size, np.asarray(raw_pcd.points).shape, pose.shape)

Install the example dependencies with:

pip install huggingface_hub numpy open3d pillow

For large repositories, downloading only selected files with huggingface_hub.hf_hub_download or CLI include/exclude patterns may be more practical than downloading the entire dataset.

Personal and Sensitive Information

The dataset is intended to contain tabletop object observations only.

Permission is required for redistribution, modification, or commercial use.

For permission requests, please contact [keep9642@korea.ac.kr].

License

Copyright (c) 2026 RILAB and SNUAILAB.

This dataset was created and maintained jointly by:

• RILAB
• SNUAILAB

The YAML metadata currently uses license: other as a placeholder. Do not publish the dataset card without replacing or explaining this value.

Citation

If you use this dataset, please cite:

@inproceedings{park2026viugic,
  title  = {Pose Estimation of Transparent Objects via Depth Completion and Confidence-Guided Registration},
  author = {Jeongeun Park, Yeoncheol Jang, Changjin Kim, YoungJoon Yoo, Sungjoon Choi},
  year   = {2026}
}