#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/edit3d-bench" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# Dataset Card for edit3d-bench

![image/png](https://huggingface.co/datasets/Voxel51/edit3d-bench/resolve/main/edit3d_bench.gif)

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 300 samples.

## 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/edit3d-bench", 
    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="edit3d-bench"  # Assign a name to the dataset for identification
)

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

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

### Dataset Description

Edit3D-Bench comprises 100 high-quality 3D models — 50 from [Google Scanned Objects (GSO)](https://arxiv.org/abs/2204.11918) and 50 from [PartObjaverse-Tiny](https://arxiv.org/abs/2305.16315). For each model, the authors provide 3 distinct editing prompts covering a range of modifications (object replacement, accessory addition, material changes, etc.), yielding 300 total editing tasks.

Each editing task includes:

- **Source 3D model** (`model.glb`) with multi-view renders, RGB/normal/mask videos
- **3D edit region** (`3d_edit_region.glb`) — a human-annotated mesh specifying which part of the source model to edit
- **2D edit mask** (`2d_mask.png`) — the edit region projected to a canonical camera view
- **2D edited reference image** (`2d_edit.png`) — generated by [FLUX.1 Fill](https://bfl.ai/blog/24-11-21-tools), showing the intended edit result
- **2D visualization** (`2d_visual.png`) — the source model rendered with the edit region removed
- **Multi-view edit region renders** — rotating video (`visual3d.mp4`) and 16 static views showing the edit region overlaid on the source model

The benchmark is designed to evaluate three aspects of 3D editing methods: (1) preservation of unedited regions (Chamfer Distance, masked PSNR/SSIM/LPIPS), (2) overall 3D quality (FID, FVD), and (3) alignment with editing conditions (DINO-I, CLIP-T).

- **Curated by:** Lin Li (Renmin University of China), Zehuan Huang (Beihang University), Haoran Feng (Tsinghua University), Gengxiong Zhuang (Beihang University), Rui Chen (Beihang University), Chunchao Guo (Tencent Hunyuan), Lu Sheng (Beihang University)
- **Language(s):** en
- **License:** MIT

### Dataset Sources

- **Repository:** [huanngzh/Edit3D-Bench](https://huggingface.co/datasets/huanngzh/Edit3D-Bench)
- **Paper:** [VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space](https://huggingface.co/papers/2508.19247) (arXiv:2508.19247)
- **Project Page:** [huanngzh.github.io/VoxHammer-Page](https://huanngzh.github.io/VoxHammer-Page/)
- **Code:** [github.com/Nelipot-Lee/VoxHammer](https://github.com/Nelipot-Lee/VoxHammer)

## Uses

### Direct Use

- Benchmarking 3D local editing methods on preservation of unedited regions, overall quality, and condition alignment
- Evaluating 3D editing pipelines that take a source model + edit region + text prompt as input
- Studying the relationship between 2D editing guidance and 3D consistency

### Out-of-Scope Use

- This dataset provides editing *specifications* (source model, edit region, reference image, prompt), not edited 3D outputs. It cannot be used directly as paired training data for 3D editing without first running an editing method.
- The 2D edited reference images are generated by FLUX.1 Fill and may contain artifacts or inconsistencies not representative of ground-truth 3D edits.

## FiftyOne Dataset Structure

### Design Rationale

The raw dataset is organized around 100 objects with 3 prompts each, spanning heterogeneous media types (video, images, 3D meshes) and multiple label relationships (masks as segmentations, normals as heatmaps, edit regions as 3D overlays). A FiftyOne **grouped dataset** is the natural fit because:

1. **Each group = one editing task** (object + prompt pair), which is the fundamental unit of the benchmark
2. **Slices enable multi-modal browsing** — toggle between video, image, and 3D views of the same editing task in the App without duplicating metadata
3. **Native label types** map directly to the dataset’s annotation types — `fo.Segmentation` for masks, `fo.Heatmap` for normal maps, `fo.GltfMesh` for 3D scenes

### Slices (6 per group, 300 groups, 1800 total samples)

| Slice                         | Media Type   | Source File                    | Labels                                                                                                                                    | Purpose                                                                                                                                        |
|-------------------------------|--------------|--------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|
| `edit_region_video` (default) | video        | `prompt_N/render/visual3d.mp4` | —                                                                                                                                         | Rotating view of the source model with the edit region mesh overlaid semi-transparently. Shows *where* the edit happens in 3D context.         |
| `source_video`                | video        | `source_model/video_rgb.mp4`   | `segmentation` (frame-level `fo.Segmentation` from `video_mask.mp4`), `normal_heatmap` (frame-level `fo.Heatmap` from `video_normal.mp4`) | Rotating RGB video of the source 3D object with per-frame object silhouette mask and surface normal overlays.                                  |
| `source_render`               | image        | `prompt_N/2d_render.png`       | `edit_mask` (`fo.Segmentation` from `2d_mask.png`)                                                                                        | Static render of the source model from the canonical editing camera, with the edit region highlighted as a segmentation overlay.               |
| `edit`                        | image        | `prompt_N/2d_edit.png`         | —                                                                                                                                         | The FLUX.1 Fill reference image showing the intended edit result.                                                                              |
| `visual`                      | image        | `prompt_N/2d_visual.png`       | —                                                                                                                                         | Source model rendered with the edit region blacked out, showing the “hole” where the edit goes.                                                |
| `scene_3d`                    | 3D           | `prompt_N/scene.fo3d`          | —                                                                                                                                         | Interactive 3D scene containing both the `source_model` and `edit_region` GLB meshes. Toggle mesh visibility to inspect the edit region in 3D. |

### Sample Fields (shared across all slices)

| Field            | Type   | Description                                                                  |
|------------------|--------|------------------------------------------------------------------------------|
| `dataset_source` | string | Source dataset: `"GSO"` or `"PartObjaverse-Tiny"`                            |
| `object_name`    | string | Object identifier (human-readable name for GSO, UUID for PartObjaverse-Tiny) |
| `prompt_index`   | int    | Editing prompt number: 1, 2, or 3                                            |
| `source_prompt`  | string | Text description of the original source model                                |
| `edit_prompt`    | string | Text description of the desired edit                                         |

### Preprocessing

The `source_video` slice requires extracted video frames for its frame-level labels. The loader (`load_into_fiftyone.py`) automatically decodes `video_mask.mp4` and `video_normal.mp4` into per-frame PNGs under `source_model/frames/`:

- **Mask frames** (`mask_0001.png` … `mask_0120.png`): Binarized at threshold 128 to remove MP4 compression artifacts. Referenced via `fo.Segmentation(mask_path=...)`.
- **Normal frames** (`normal_0001.png` … `normal_0120.png`): Full RGB surface normals preserved as-is. Referenced via `fo.Heatmap(map_path=...)`.

This extraction is idempotent — frames are only written if they don’t already exist on disk.

## Dataset Creation

### Curation Rationale

Existing 3D editing benchmarks lack labeled editing regions, making it difficult to objectively evaluate how well methods preserve unedited parts of a model. Edit3D-Bench was constructed specifically to address this gap by providing human-annotated 3D editing regions for each editing task.

### Source Data

#### Data Collection and Processing

- **3D models**: 50 models from Google Scanned Objects (GSO), a collection of high-quality 3D scanned household items, and 50 from PartObjaverse-Tiny, a subset of Objaverse with part-level annotations
- **Editing prompts**: 3 prompts per model, covering modifications such as object replacement, accessory addition, and material/texture changes
- **3D edit regions**: Human-annotated 3D meshes specifying the precise spatial extent of each edit
- **2D reference edits**: Generated by rendering the source model from a canonical viewpoint, then inpainting the edit region using FLUX.1 Fill conditioned on the edit prompt
- **Multi-view renders**: Each source model rendered from a 16-camera rig (2 elevation rings of 8 azimuth angles at 1024x1024 resolution), plus 120-frame rotating videos for RGB, normals, and object mask

#### Who are the source data producers?

The 3D models originate from GSO (Google) and PartObjaverse-Tiny (community-contributed Objaverse subset). The editing annotations (regions, prompts, reference edits) were produced by the VoxHammer paper authors.

### Annotations

#### Annotation process

The 3D edit regions were manually annotated by the paper authors as 3D meshes (`3d_edit_region.glb`) that define the spatial volume to be edited. The corresponding 2D masks (`2d_mask.png`) are projections of these 3D regions onto the canonical camera view. The 2D edited reference images were generated automatically using FLUX.1 Fill.

#### Personal and Sensitive Information

The dataset contains only 3D models of everyday objects (toys, furniture, animals, vehicles, etc.). It does not contain personal, sensitive, or private information.

## Bias, Risks, and Limitations

- The 2D edited reference images are generated by FLUX.1 Fill, not manually created. They may contain artifacts, hallucinations, or inconsistencies inherent to the image inpainting model.
- The dataset covers a limited range of object categories (household items, toys, furniture) and editing types. Results may not generalize to all 3D editing scenarios.
- Edit prompts are in English only.

## Citation

**BibTeX:**

```bibtex
@article{li2025voxhammer,
  title = {VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space},
  author = {Li, Lin and Huang, Zehuan and Feng, Haoran and Zhuang, Gengxiong and Chen, Rui and Guo, Chunchao and Sheng, Lu},
  journal = {arXiv preprint arXiv:2508.19247},
  year = {2025},
  url = {https://huggingface.co/papers/2508.19247}
}
```