README.md

---
annotations_creators: []
language: en
size_categories:
- n<1K
task_ids: []
pretty_name: TransPhy3D
tags:
- fiftyone
- group
dataset_summary: >




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 51
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include 'max_samples', etc

  dataset = load_from_hub("Voxel51/TransPhy3D")


  # Launch the App

  session = fo.launch_app(dataset)

  ```
license: apache-2.0
---

# Dataset Card for TransPhy3D

![image/png](transphys3d.gif)


**TransPhy3D (FiftyOne)** is a grouped FiftyOne dataset built from a random subset of the
[TransPhy3D](https://huggingface.co/datasets/Daniellesry/TransPhy3D) **test** split.
Each group contains one RGB video sequence plus a merged 3D reconstruction of the same
scene.
 

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from huggingface_hub import snapshot_download


# Download the dataset snapshot to the current working directory

snapshot_download(
    repo_id="Voxel51/TransPhy3D", 
    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="TransPhy3D"  # Assign a name to the dataset for identification
)

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


```


## Dataset Details

### Dataset Description

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 51 samples.

The source dataset provides synthetic transparent/reflective object scenes rendered in
Blender/Cycles with RGB video frames, 16-bit depth maps, surface normal maps, and per-frame
camera calibration.

- **Source dataset:** [Daniellesry/TransPhy3D](https://huggingface.co/datasets/Daniellesry/TransPhy3D)
- **Source split used:** `test`
- **License:** Apache 2.0 (source dataset license)

### Dataset Sources

- **Repository:** https://huggingface.co/datasets/Daniellesry/TransPhy3D
- **Paper:** [Diffusion Knows Transparency (arXiv:2512.23705)](https://arxiv.org/abs/2512.23705)
- **Project page:** https://daniellli.github.io/projects/DKT/
- **GitHub:** https://github.com/Daniellli/DKT

---

## Sampling From the Source Test Set

The full TransPhy3D test split contains **131** WebDataset tar sequences.

This FiftyOne dataset uses a **random subset of 51 sequences** (~39% of the test split):

| Step | Count | Notes |
|------|------:|-------|
| Initial random sample | 33 | ~25% of 131, `random.seed(42)` |
| Additional sequences | 18 | disjoint from the first 33, same seed |
| **Total in this dataset** | **51** | listed in `data/selected_tars.txt` |

Scene type breakdown in this sample:

- **39** `materials` sequences (`0826_*`)
- **12** `table_with_robot` sequences (`0827_table_with_robot_*`)

Each selected sequence contains **121 frames**.

---

## FiftyOne Dataset Structure

**Dataset name:** `TransPhy3D`

**Media type:** `group`

**Default group slice:** `video`

### Groups and slices

There are **51 groups**. Each group has two linked slices:

| Slice | Media type | `filepath` | Description |
|-------|------------|------------|-------------|
| `video` | `video` | `rgb.mp4` | RGB video assembled from source frames |
| `reconstruction` | `3d` | `scene.fo3d` | Merged RGB-colored point cloud for the whole scene |

Switch slices in the FiftyOne App to view the video annotations or the 3D reconstruction
for the same sequence.

### Sample-level fields

Present on both slices unless noted:

| Field | Type | Description |
|-------|------|-------------|
| `sequence_id` | string | Sequence prefix, e.g. `0826_0004` |
| `scene_type` | string | `materials` or `table_with_robot` |
| `tags` | list | `["test"]` on video; `["test", "reconstruction"]` on 3D slice |

The import scripts also set `source_tar` and `frame_count` on the video slice when samples
are built.

### Frame-level fields (video slice only)

Each video sample has **121 frame documents** (**6,171** total across the dataset).

| Field | FiftyOne type | Description |
|-------|---------------|-------------|
| `frame_id` | int | Frame index from source metadata |
| `sequence_id` | string | Sequence id for this frame |
| `max_depth` | float | Depth scale factor from `depth.json` |
| `depth_map` | `Heatmap` | 16-bit depth PNG on disk (`range=[0, 65535]`) |
| `normal_map` | `Heatmap` | RGB-encoded normal PNG on disk |
| `depth_json` | dict | Parsed contents of `depth.json` |
| `camera_extrinsics` | list | 4×4 extrinsics matrix |
| `camera_intrinsics` | list | 3×3 normalized intrinsics matrix |

There are **no object detection or segmentation labels**. Supervision is dense per-pixel depth, normals, and camera calibration.

---

## 3D Reconstruction

Each sequence has **one merged point cloud** for the whole scene (not one cloud per frame).

Process (`reconstruct_scenes.py`):

1. Decode 16-bit depth using paired `depth.json`.
2. Convert normalized intrinsics to pixel-space `K`.
3. Back-project depth pixels into world coordinates using per-frame extrinsics.
4. Color each 3D point from the matching RGB video frame.
5. Merge all 121 frames into one point cloud.
6. Downsample with Open3D voxel grid (`voxel_size=0.01` by default).
7. Write `scene.pcd` and wrap it in `scene.fo3d` for the `reconstruction` group slice.

Default reconstruction settings:

| Parameter | Default | Purpose |
|-----------|---------|---------|
| `--stride` | `4` | Subsample every 4th pixel during back-projection |
| `--voxel-size` | `0.01` | World-space voxel downsampling |
| `--max-depth-ratio` | `0.999` | Drop saturated far-plane depth values |

The reconstruction is a **point cloud**, not a mesh. It is intended for interactive 3D viewing in FiftyOne, not watertight surface reconstruction.

---

## How to Build / Reload the Dataset

### Prerequisites
Download the selected source tars from [Daniellesry/TransPhy3D](https://huggingface.co/datasets/Daniellesry/TransPhy3D)

Run the two scripts in this order:

**Step 1 — Extract assets and build video samples**

```bash
python import_transphy3d.py --overwrite
```

This:

- Reads WebDataset tars from `data/tars/test/`
- Writes processed PNG/JSON assets and `rgb.mp4` files
- Creates FiftyOne video samples with per-frame heatmaps and metadata

**Step 2 — Reconstruct 3D scenes and build grouped dataset**

```bash
python reconstruct_scenes.py --build-dataset --overwrite
```

This:

- Builds merged RGB point clouds (`scene.pcd`, `scene.fo3d`)
- Replaces/creates the grouped FiftyOne dataset `TransPhy3D` with `video` + `reconstruction` slices

---

## Citation

If you use the source TransPhy3D dataset, cite:

```bibtex
@article{dkt2025,
  title   = {Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation},
  author  = {Shaocong Xu and Songlin Wei and Qizhe Wei and Zheng Geng and Hong Li and Licheng Shen and Qianpu Sun and Shu Han and Bin Ma and Bohan Li and Chongjie Ye and Yuhang Zheng and Nan Wang and Saining Zhang and Hao Zhao},
  journal = {https://arxiv.org/abs/2512.23705},
  year    = {2025}
}
```