README.md

---
pretty_name: AssemLM 2.0 Processed Assembly Dataset
license: other
tags:
  - robotic-assembly
  - point-cloud
  - 3d-vision
  - multimodal
  - hdf5
configs:
  - config_name: biasassembly_preview
    data_dir: preview/biasassembly
    default: true
  - config_name: ikea_preview
    data_dir: preview/ikea
  - config_name: partnet_preview
    data_dir: preview/partnet
  - config_name: partnext_preview
    data_dir: preview/partnext
  - config_name: twobytwo_preview
    data_dir: preview/twobytwo
---

# **AssemLM 2.0 Processed Assembly Dataset**

**Related links:** [AssemLM 1.0 paper (arXiv)](https://arxiv.org/abs/2604.08983) · [AssemLM official website](https://assemlmhome.github.io/)

This repository provides HDF5 datasets for **AssemLM 2.0 training**. Each sample
contains point clouds for a moving part and a fixed/base part, two assembly
manual images, and an object category. The `preview/` directory contains a
**small image-based preview subset** configured for display in the Hugging Face
Dataset Viewer.

## Dataset files

| File | Train samples | Test samples | Total | Manual images | Approx. size |
|---|---:|---:|---:|---|---:|
| **`assemlm_biasassembly.hdf5`** | 11,653 | 1,285 | 12,938 | Freestyle | 4.33 GiB |
| **`assemlm_ikea.hdf5`** | 0 | 652 | 652 | Freestyle | 0.20 GiB |
| **`assemlm_partnet.hdf5`** | 51,155 | 12,897 | 64,052 | Freestyle | 8.45 GiB |
| **`assemlm_partnext.hdf5`** | 54,743 | 2,980 | 57,723 | Freestyle | 5.59 GiB |
| **`assemlm_twobytwo.hdf5`** | 301 | 140 | 441 | Lineart | 0.12 GiB |
| **Total** | **117,852** | **17,954** | **135,806** | — | **approximately 18.7 GiB** |

## Lightweight subsets for the AssemLM 2.0 GUI

Opening one of the large collections in the **AssemLM 2.0 GUI** is slow: the
dataset panel walks every asset of the selected split before the file becomes
usable, which delays both browsing and the inference stage. The `sub500/`
directory therefore ships smaller versions of the three largest datasets so that
the GUI loads them within seconds:

| File | Train samples | Test samples | Total | Manual images | Approx. size |
|---|---:|---:|---:|---|---:|
| **`sub500/assemlm_biasassembly_sub500.hdf5`** | 500 | 500 | 1,000 | Freestyle | 1.88 GiB |
| **`sub500/assemlm_partnet_sub500.hdf5`** | 500 | 500 | 1,000 | Freestyle | 1.88 GiB |
| **`sub500/assemlm_partnext_sub500.hdf5`** | 500 | 500 | 1,000 | Freestyle | 1.88 GiB |

Each subset keeps the exact structure of its full counterpart — the same
`split/` and `objs/` layout, the same fields, manual image keys, and point-cloud
sizes — so the GUI, the dataloader, and the evaluation scripts accept them
without any change; they simply contain fewer assets. Every object category of
the corresponding full dataset is represented (biasassembly 20, partnet 3,
partnext 50), so category browsing and per-category inspection behave exactly as
they do on the full files.

These subsets are convenience artifacts for interactive use only: the five full
files in the table above remain the reference datasets for training and
evaluation.

## Hugging Face preview samples

To make the collection inspectable in the Hugging Face Dataset Viewer, the
`preview/` directory contains a small, separate image-based preview. For every
HDF5 file, up to 20 samples are exported from `train` and up to 20 samples from
`test`. 

The current IKEA file has no training samples, so `preview/ikea/train` contains
an empty `metadata.jsonl`; its preview consists of 20 diverse test samples.

```text
preview/
├── biasassembly/
│   ├── train/
│   │   ├── metadata.jsonl
│   │   ├── 00000_manual_pair.png
│   │   ├── 00000_base.png
│   │   ├── 00000_assemble.png
│   │   └── ...
│   ├── test/
│   └── manifest.json
├── ikea/
├── partnet/
├── partnext/
└── twobytwo/
```

Each `*_manual_pair.png` is a side-by-side image containing the base-part and
assembled views, so it can be rendered as the primary image column by the
Dataset Viewer. The accompanying `metadata.jsonl` records the asset ID,
category, manual type, original image filenames, point-cloud filenames, and
point counts. The individual PNG and NumPy files are retained for download and
local inspection.

These preview files are **additional artifacts only**: the **five full HDF5
files** remain unchanged and continue to provide the complete
training/evaluation data.

## HDF5 structure

Each HDF5 file contains two top-level groups:

```text
<dataset>.hdf5
├── split/
│   ├── train       # List of training asset IDs
│   └── test        # List of test asset IDs
└── objs/
    └── <asset_name>/
        ├── partA-pc
        ├── base_partB-pc
        ├── category
        ├── image_base_<manual>
        └── image_assemble_<manual>
```

The `<manual>` suffix is selected by dataset:

- **`lineart`** is used only by **`assemlm_twobytwo.hdf5`**;
- The other four datasets use **`freestyle`**.

During AssemLM 2.0 inference, the model instruction is constructed from the category:

```text
Assemble the {category} object
```

## Sample fields

| Field | Typical shape / type | Description |
|---|---|---|
| `partA-pc` | `[N, 3]`, `float32` | Moving-part point cloud |
| `base_partB-pc` | `[N, 3]`, `float32` | Fixed/base-part point cloud |
| `category` | Scalar string | Object category |
| `image_base_freestyle` | `[576, 576, 3]`, `uint8` | Base-part freestyle rendering |
| `image_assemble_freestyle` | `[576, 576, 3]`, `uint8` | Assembled freestyle rendering |
| `image_base_lineart` | `[576, 576, 3]`, `uint8` | Base-part lineart rendering |
| `image_assemble_lineart` | `[576, 576, 3]`, `uint8` | Assembled lineart rendering |

Each sample retains one complete image pair appropriate for its dataset. The point count `N` is 1,000 or 1,024. The AssemLM dataloader
validates the point clouds and, by default, resamples them to 1,024 points.

## Loading with h5py

```python
import h5py

path = "assemlm_twobytwo.hdf5"
with h5py.File(path, "r") as f:
    train_ids = f["split/train"][:]
    asset_id = (
        train_ids[0].decode()
        if isinstance(train_ids[0], bytes)
        else str(train_ids[0])
    )
    asset_key = asset_id.replace("/", "_")
    sample = f["objs"][asset_key]

    moving_pc = sample["partA-pc"][:]
    fixed_pc = sample["base_partB-pc"][:]
    category = sample["category"][()]
    base_image = sample["image_base_lineart"][:]
    assemble_image = sample["image_assemble_lineart"][:]
```

For a freestyle dataset, replace the two lineart keys with
`image_base_freestyle` and `image_assemble_freestyle`.

## Intended use

This dataset is intended for:

- **AssemLM 2.0 training, evaluation, and reproducibility studies**;
- **3D pose prediction for robotic part assembly**;
- **Multimodal spatial reasoning from point clouds and rendered images**.

## Licensing

This release aggregates several upstream 3D assembly datasets, and every subset
inherits the terms of the collection it was derived from:

| Subset | Upstream dataset | License |
|---|---|---|
| `assemlm_ikea*.hdf5` | IKEA-Manual | CC BY 4.0 |
| `assemlm_partnet.hdf5` | PartNet | MIT |
| `assemlm_partnext.hdf5` | PartNeXt | see the PartNeXt dataset release (its data is built on Objaverse, ABO, and 3D-Future) |
| `assemlm_biasassembly*.hdf5` | BiAssemble | see the corresponding dataset release |
| `assemlm_twobytwo.hdf5` | Two by Two | MIT |

The point clouds and manual renderings in this repository were re-processed and
re-rendered for AssemLM 2.0; this release does not supersede any upstream
license, and users remain responsible for complying with the terms of the
original datasets. If you hold the rights to one of these datasets and would
like an attribution adjusted or content removed, please open an issue and we
will respond.

## Citation

If this project is useful to you, please consider citing:

```bibtex
@article{jing2026assemlm,
  title={AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly},
  author={Jing, Zhi and Qiao, Jinbin and Lu, Ouyang and Ao, Jicong and Qiu, Shuang and Xu, Huazhe and Jiang, Yu-Gang and Bai, Chenjia},
  journal={arXiv preprint arXiv:2604.08983},
  year={2026}
}
```

## Acknowledgements

We thank the authors of the following datasets and research works for their
public resources and important contributions:

- IKEA-Manual;
- PartNet;
- PartNeXt;
- BiAssemble;
- Two by Two.

We also thank the authors of Manual-PA and Manual2Skill. Their research on
understanding assembly manuals, 3D part assembly, and robotic skill learning
provided important references for the construction and application of this
dataset.

When using the datasets or research outputs listed above, please follow the
licenses and citation requirements of the respective papers, datasets, and
code repositories.