| --- |
| pretty_name: Act3D-data |
| license: other |
| task_categories: |
| - visual-question-answering |
| tags: |
| - 3d |
| - embodied |
| - spatial-reasoning |
| - reinforcement-learning |
| configs: |
| - config_name: rl |
| data_files: |
| - split: train |
| path: annotations/rl_train.parquet |
| - config_name: sft |
| data_files: |
| - split: train |
| path: annotations/sft_train.parquet |
| - split: validation |
| path: annotations/sft_val.parquet |
| --- |
| |
| # Act3D-data |
|
|
| Training data for **Act3D: Learning to Think and Explore in 3D World** |
| ([paper](https://arxiv.org/abs/2610.21783), [code](https://github.com/williamium3000/verl-projects/tree/3d-publish)). |
|
|
| This repository contains **annotations only**: questions, answers, answer boxes, camera poses and |
| exploration trajectories. It contains **no scene data and no images**. |
| - Every scene comes from ScanNet, ARKitScenes or Matterport3D. Their licenses do not allow us to |
| redistribute meshes or anything rendered from them. |
| - You download the meshes yourself from the original providers. |
| - `build_images.py` then renders every image locally. The trajectory images come out exactly as |
| they were when we trained. |
|
|
| ## Contents |
|
|
| | file | rows | what it is | |
| |---|---|---| |
| | `annotations/rl_train.parquet` | 51,543 | RL (GRPO) questions: one question + one start pose per row | |
| | `annotations/sft_train.parquet` | 4,949 | SFT trajectories (think / move / observe / answer) | |
| | `annotations/sft_val.parquet` | 213 | SFT validation trajectories | |
| | `annotations/arkit_eval.jsonl` | 1,000 | ARKit evaluation questions (ours; ARKitScenes 3DOD `Training` scenes, room-disjoint from RL training, see below), read by the code's `act3d/eval/prepare_eval.py` | |
| | `annotations/meshes.sha256` | | checksums of every mesh file the training annotations use | |
| | `build_images.py` | | renders the images and writes the training parquets | |
| | `prep_mp3d_regions.py` | | crops Matterport3D houses into the per-room meshes used here | |
|
|
| RL questions by source: |
|
|
| | `data_source` | rows | scenes | questions from | |
| |---|---|---|---| |
| | `scanqa` | 25,563 | ScanNet | ScanQA (Azuma et al., CVPR 2022) | |
| | `genqa` | 8,591 | ScanNet | ours: generated from the scene geometry, verified by an LLM | |
| | `mp3d` | 9,855 | Matterport3D regions | MMScan (Lyu et al., NeurIPS 2024), MP3D subset: **ids only**, see below | |
| | `genqa_ark` | 7,534 | ARKitScenes 3DOD | ours: generated from the scene geometry, verified by an LLM | |
|
|
| ### Fields |
|
|
| **RL** (`rl_train.parquet`): `index`, `data_source`, `scene_id`, `mesh_path`, `question`, `answers` |
| (JSON list), `answer_boxes` (JSON list of `[cx, cy, cz, sx, sy, sz]` in metres, the boxes of the |
| objects the answer refers to; used by the exploration reward), `qtype`, `ground_truth`, and |
| `start_eye` / `start_at` (the start camera: position and look-at point, z up). |
|
|
| **SFT** (`sft_*.parquet`): `qid`, `scene_id`, `src`, `mesh_path`, `messages` (system / user / |
| assistant / tool turns; every `<image>` is one rendered view), `start_eye` / `start_at`, |
| `start_pose_index`, `actions` (the action applied by each tool turn that returned an image), |
| `bounds_min` / `bounds_max` (the scene box used to clip camera moves) and `n_images`. |
|
|
| `mesh_path` is always relative to `$ACT3D_ROOT`. |
|
|
| **MMScan questions are not included.** |
| - The Matterport3D questions come from MMScan, which is distributed under its own access terms. |
| - Those rows carry only `mmscan_id`, the `id` of the entry in MMScan's `LLaVA-3D_MMScan_QA.json`: |
| - in RL rows, `question`, `answers` and `ground_truth` are empty; |
| - in SFT rows, the question in the user turn is `{MMSCAN_QUESTION}`, and the MMScan answer is |
| `{MMSCAN_ANSWER}` (in the final `<answer>`, and in the reasoning where it is quoted verbatim). |
| - `build_images.py --mmscan` fills them back in from your own copy of MMScan. |
| |
| ## Setup |
|
|
| ### 1. Get the scene meshes |
|
|
| Request access from each provider and accept its terms of use. Then place the files below under |
| `$ACT3D_ROOT`. Only these files are read; you don't need the RGB-D streams. |
|
|
| ``` |
| $ACT3D_ROOT/ |
| ├── annotations/ # this repository |
| ├── scannet/scans/<scene_id>/ |
| │ ├── <scene_id>_vh_clean.ply # dense mesh: required, it is what we rendered |
| │ └── <scene_id>_vh_clean_2.ply # decimated mesh: the name mesh_path points to |
| ├── arkitscenes/3dod/Training/<video_id>/ |
| │ └── <video_id>_3dod_mesh.ply |
| └── mp3d/regions/ |
| └── <house_id>_region<N>.ply # made by prep_mp3d_regions.py |
| ``` |
|
|
| - **ScanNet** v2 (<https://github.com/ScanNet/ScanNet>): sign the ScanNet Terms of Use. With the official |
| `download-scannet.py`, fetch `--type _vh_clean.ply` and `--type _vh_clean_2.ply` for the scenes |
| listed in `annotations/meshes.sha256`, into `scannet/scans/`. |
| The renderer automatically uses `_vh_clean.ply` next to the `_vh_clean_2.ply` that `mesh_path` names. |
| Without the dense mesh, renders will not match the training data. |
| - **ARKitScenes** (<https://github.com/apple/ARKitScenes>): download the 3DOD `Training` split with the |
| official `download_data.py 3dod --split Training`. Keep `<video_id>/<video_id>_3dod_mesh.ply` |
| under `arkitscenes/3dod/Training/`. |
| - **Matterport3D** (<https://niessner.github.io/Matterport/>): sign the Matterport3D Terms of Use and |
| download `house_segmentations` for the houses in `annotations/meshes.sha256`. Then crop the |
| rooms with: |
| ```bash |
| python prep_mp3d_regions.py --scans /path/to/v1/scans --root $ACT3D_ROOT |
| ``` |
| This writes each `<house>_region<N>.ply`: the house mesh cropped to that region's box from |
| `<house>.house`, plus 0.15 m. |
|
|
| Check your files (from `$ACT3D_ROOT`): |
| ```bash |
| cd $ACT3D_ROOT && sha256sum -c annotations/meshes.sha256 --quiet |
| ``` |
| - ScanNet and ARKitScenes files should match exactly. |
| - The MP3D crops can differ byte-wise across Open3D versions (we used Open3D 0.19); renders are unaffected. |
|
|
| ### 2. Get the MMScan questions and ScanQA val |
|
|
| Request access to MMScan (<https://github.com/OpenRobotLab/EmbodiedScan>, Hugging Face |
| `3dllm/MMScan-beta`) and download `LLaVA-3D_MMScan_QA.json`. |
|
|
| RL validation is the full official ScanQA v1.0 val split (4,675 questions; not redistributed here). |
| Download `ScanQA_v1.0_val.json` from <https://github.com/ATR-DBI/ScanQA> (dataset link in |
| `docs/dataset.md`) to `$ACT3D_ROOT/benchmarks/scanqa/ScanQA_v1.0_val.json`, and get its 71 ScanNet |
| scenes the same way as the training scenes. |
|
|
| ### 3. Render the images |
|
|
| With the Act3D code checked out (it provides the renderer, `act3d/act3d/env.py`): |
|
|
| ```bash |
| export ACT3D_ROOT=/path/to/act3d_data |
| PYTHONPATH=/path/to/Act3D/act3d python build_images.py --mmscan /path/to/LLaVA-3D_MMScan_QA.json --workers 32 |
| ``` |
|
|
| This runs on CPU and writes the files the training scripts read: |
|
|
| ``` |
| $ACT3D_ROOT/sft/train.parquet, sft/val.parquet # messages + images (verl multi-turn SFT format) |
| $ACT3D_ROOT/rl/train.parquet, rl/val.parquet # prompt + start view + extra_info + reward_model |
| # (rl/val.parquet = the full ScanQA val split) |
| ``` |
|
|
| - **SFT images**: each trajectory is replayed from its start pose with the recorded actions. On our |
| machine this reproduced all training images pixel-exactly (see "Verification"). |
| - **RL start views**: these are rendered with the same renderer at the start pose. The views baked |
| into our original RL parquets came from an earlier pipeline that used the raw ScanNet camera |
| (`.sens` intrinsics and 6-DoF pose). The release renders the same position and direction with |
| the Act3D renderer instead, so the start view matches the tool's own renders. |
|
|
| Then follow the training and evaluation steps in the code README. |
|
|
| ## License and attribution |
|
|
| - **Our annotations** (GenQA questions, answer boxes, trajectories, poses): CC BY-NC-SA 4.0. |
| - **ScanQA questions** follow ScanQA's license. |
| - **MMScan questions** are not redistributed: only their ids are, and you get the text from MMScan under its terms. |
| - **Scenes**: you must obtain ScanNet, ARKitScenes and Matterport3D yourself and follow their terms. |
| Nothing derived from their imagery is included here. |
|
|
| Please cite Act3D and the source datasets: |
|
|
| ```bibtex |
| @article{li2026act3d, |
| title = {Learning to Think and Explore in 3D World}, |
| author = {Li, Yijiang and Wang, Bingyang and Vasconcelos, Nuno}, |
| journal = {arXiv preprint arXiv:2610.21783}, |
| year = {2026} |
| } |
| ``` |
| ScanNet (Dai et al., CVPR 2017), ARKitScenes (Baruch et al., NeurIPS 2021 Datasets and Benchmarks), |
| Matterport3D (Chang et al., 3DV 2017), ScanQA (Azuma et al., CVPR 2022), MMScan (Lyu et al., NeurIPS 2024). |
|
|
