---
title: 'Nano3D-Edit-100k: 3D Editing Dataset'
url: https://www.emergentmind.com/topics/nano3d-edit-100k-dataset
type: topic
---

# Nano3D-Edit-100k: 3D Editing Dataset

Nano3D-Edit-100k is a large-scale, fully automated, and mask-free dataset for paired 3D object editing, comprising over 100,000 high-quality source–target 3D editing examples. Designed to support feed-forward, high-fidelity, and controllable 3D editing research, it is constructed using the Nano3D framework, which operates without human-annotated masks or manual intervention. Each edit is generated through a training-free pipeline that leverages front-view renderings, automated text instructions, and novel merging techniques to ensure structural fidelity and category diversity across object types and editing operations [2510.15019].

## 1. Dataset Composition and Coverage

Nano3D-Edit-100k contains 100,000 editing examples. Each example consists of:

- A source 3D asset in the TRELLIS sparse voxel grid and structured latent (“SLAT”) form.
- A source front-view rendering at 256 × 256 px.
- A text editing instruction, generated using fixed templates (“add X to Y”, “remove X from Y”, “replace X with Z”).
- An edited front-view image, produced by Nano-Banana or Flux-Kontext editors.
- A target (edited) 3D asset, stored as a SLAT file and optionally as a GLB/OBJ mesh (via FlexiCube).
- A JSON metadata file providing category, camera intrinsics and extrinsics, instruction, runtime-computed merging mask, and quality flags.

The dataset balances object categories across ten high-level classes, with the assignment automated by Qwen2.5-VL. Editing operations (addition, removal, replacement) are equally represented. The following table summarizes category and operation distributions:

| Category           | Example Count | Percentage |
|--------------------|--------------|------------|
| Human              | 20,755       | 20.75%     |
| Weapon             | 11,021       | 11.03%     |
| Furniture          | 10,442       | 10.45%     |
| Personal Item      | 10,277       | 10.28%     |
| Animal             | 10,186       | 10.19%     |
| Vehicle            | 9,376        | 9.38%      |
| Building           | 9,005        | 8.97%      |
| Plant              | 4,441        | 4.45%      |
| Electronic Device  | 5,283        | 5.29%      |
| Else               | 6,593        | 6.60%      |

Each operation—addition, removal, replacement—has approximately 33,000 examples.

## 2. Automated Data Generation Pipeline

The construction of Nano3D-Edit-100k proceeds via a five-stage, training-free automated pipeline:

1. Sample a front-view image \(I_{src}\) from a large image pool.
2. Generate a textual edit instruction from \(I_{src}\) using a vision-language model (VLM) like Qwen2.5-VL, choosing randomly among three fixed prompt templates.
3. Reconstruct the source 3D asset via TRELLIS, voxelizing at a \(64^3\) grid and inferring the SLAT representation.
4. Produce an edited front-view image using the pretrained Nano-Banana or Flux-Kontext image editor, conditioned on the instruction.
5. Generate the edited 3D asset using Nano3D.edit(), which applies FlowEdit and region-aware Voxel/Slat-Merge strategies.
6. Apply quality filtering to images and meshes; only passing examples are retained in the dataset.

Editing is performed without masks; all region selections are determined algorithmically based on object structure changes.

## 3. Region-Aware Merging and Editing Methodology

Editing in Nano3D follows these principal stages:

- **FlowEdit**: Source (\(I_{src}\)) and target (\(I_{tgt}\)) renderings condition the TRELLIS model. Rather than standard flow-based sampling, Nano3D integrates source and target velocity fields along ODE trajectories, interpolating with a mixing schedule \(\alpha_t\), yielding an edited voxel grid \(V_{edited}\).

- **Voxel-Merge**: Compute a voxel-wise XOR (\(g(i) = s_{src}(i) \oplus s_{tgt}(i)\)) on sparse occupancies \(s_{src}, s_{tgt} \in \{0,1\}^{64^3}\). Connected components \(g_j\) are identified in 6/18/26-neighborhoods and filtered by a threshold \(\tau=100\) voxels. The merging mask \(M\) selects components above this threshold:
  \[
    M(i) = 
      \begin{cases}
        1 & \text{if } i \in \bigcup_{j:|g_j|>\tau} g_j \\
        0 & \text{otherwise}
      \end{cases}
  \]
  Final voxel merging is performed as:
  \[
    V_{merged}(p) = \alpha(p) V_{edited}(p) + (1 - \alpha(p)) V_{src}(p)
  \]
  with \(\alpha(p) = 1\) if \(M(i_p) = 1\).

- **Latent-Merge (SLAT)**: The merged voxel grid and edited image are used in Stage 2 TRELLIS to generate new structured latents \(z_{tgt}\). Merging in latent space yields:
  \[
    z_{merged} = M \odot z_{tgt} + (1 - M) \odot z_{src}
  \]
  Decoding the merged SLAT with the TRELLIS VAE ensures globally consistent 3D asset generation.

No human-drawn masks are included; mask computation is internal and not required at test time.

## 4. Dataset Statistics, Formatting, and Splits

Nano3D-Edit-100k provides the following file types per example:

- **SLAT file** in PyTorch (.pt) format: contains the sparse voxel grid and array of local latent vectors \((z_i \in \mathbb{R}^C, p_i \in \mathbb{R}^3)\).
- **GLB/OBJ meshes** (optional): produced via the FlexiCube converter for use in downstream applications.
- **Front-view images**: both source and edited renderings, 256 × 256 px in .png format.
- **Metadata JSON**: includes object category, operation type, edit instruction, camera intrinsics/extrinsics, merging mask, voxel occupation count, and quality flag.

Dataset characteristics at release:

- Polygon count (GLB): median ≈ 30,000 faces, range 5,000–200,000.
- Median voxel occupancy: 1.2 k active voxels per 3D object (on \(64^3\) grid).
- Front-view resolution: 256² px; additional multi-view renderings at 128² px.
- Stratified split recommended: 80% train (≥ 80k), 10% validation (10k), 10% test (10k), with preservation of category and operation balance.

## 5. Evaluation Protocols and Baseline Metrics

Nano3D-Edit-100k includes standardized evaluation protocols and public baseline results. Each 3D asset is rendered into \(N\) multi-view images (e.g., 8 azimuthal views). Recommended metrics:

- **Chamfer Distance (CD)**: Quantifies structure preservation in non-edited regions; calculated as
  \[
    \text{CD} = \sum_{x\in P_{src}^U} \min_{y\in P_{tgt}^U} \|x - y\|^2 + \sum_{y\in P_{tgt}^U} \min_{x\in P_{src}^U} \|y - x\|^2
  \]
  where \(P_{src}^U, P_{tgt}^U\) are point samples from unedited voxels.

- **Target Semantic Alignment (DINO-I score)**: Compares edited image semantics using DINO-I between rendered edited views and the edited image.

- **Generation Fidelity & Diversity (FID score)**: Computes Fréchet Inception Distance between rendered edited multiview images and ground-truth.

Reported baseline metrics (100 randomly chosen examples):

| Method     | CD     | DINO-I | FID    |
|------------|--------|--------|--------|
| Tailor3D   | 0.037  | 0.759  | 140.93 |
| Vox-E      | —      | 0.782  | 117.12 |
| TRELLIS    | 0.019  | 0.901  | 49.57  |
| Nano3D     | 0.013  | 0.950  | 27.85  |

Semantic alignment at the dataset level uses CLIPScore and ViLT R-Precision. Nano3D-Edit-100k achieves CLIPScore 39.71 and ViLT R-Precision (R@5: 45.3%, R@10: 52.4%), outperforming the prior 3D-Alpaca dataset (28.42; 33.6%, 40.2%).

## 6. Research Implications and Applications

Nano3D-Edit-100k addresses challenges in paired 3D editing supervision, absence of large-scale high-fidelity 3D edit pairs, and the need for mask-free pipelines. By providing diverse, balanced, and high-throughput data, it enables the systematic evaluation and development of fully feed-forward 3D editing models. Its mask-free, automated approach makes it suitable for advancing real-time and controllable 3D content generation in domains such as interactive content creation, animation, gaming, and robotics. A plausible implication is that the dataset will facilitate the transition to third-stage 3D editing: learning feed-forward, high-recall, real-time systems trained on large-scale, richly paired 3D supervision [2510.15019]. All code for data loading, rendering, and metric computation is announced for release alongside the data, fostering reproducibility and benchmarking in the 3D editing community.

Source: https://www.emergentmind.com/topics/nano3d-edit-100k-dataset