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--- |
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license: mit |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- image-to-3d |
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- robotics |
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- image-feature-extraction |
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- depth-estimation |
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- image-to-text |
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- other |
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modalities: |
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- image |
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- tabular |
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- text |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: semantic_class |
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dtype: string |
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- name: transform |
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dtype: string |
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- name: Tx |
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dtype: float32 |
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- name: Ty |
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dtype: float32 |
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- name: Tz |
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dtype: float32 |
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- name: rot_x |
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dtype: float32 |
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- name: rot_y |
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dtype: float32 |
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- name: rot_z |
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dtype: float32 |
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- name: rot_w |
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dtype: float32 |
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splits: |
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- name: train |
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num_bytes: 4106560699 |
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num_examples: 16000 |
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- name: validation |
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num_bytes: 510934045 |
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num_examples: 2000 |
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- name: test |
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num_bytes: 513367561 |
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num_examples: 2000 |
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download_size: 2568917592 |
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dataset_size: 5130862305 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
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# A Synthetic Dataset for Visual Perspective Taking |
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This dataset ("AnonymousDataset") accompanies the submission **"Towards Social Foundation Models: A Framework and Synthetic Dataset for Grounding Visual Perspective Taking in Robots"**. |
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It is a large-scale synthetic resource designed for training socio-cognitive foundational models for robotics, specifically for the task of **Visual Perspective Taking (VPT)**. The core objective is to enable a robot to infer an object's precise 6-DOF pose (position and orientation) relative to another agent's viewpoint, given a single RGB image. |
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The dataset was procedurally generated using **NVIDIA Isaac Sim** and **Omniverse Replicator**, providing high-fidelity RGB images paired with perfect ground-truth pose annotations. |
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--- |
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## Dataset Details |
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### Dataset Summary |
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This dataset serves to explore the viability of using high-fidelity synthetic data as a scalable and cost-effective alternative for metric spatial grounding in the context of Visual Perspective Taking. |
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The data consists of renders of a target object (**mug**) placed on a tabletop in a shared workspace scene containing a humanoid agent (**x-bot**). For each rendered image, the dataset contains separate entries for each entity, providing its semantic class and exact 6-DOF pose relative to the camera. |
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* **Total Examples:** 20,000 (derived from 10,000 unique scenes) |
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* **Objects:** `mug`, `xbot_humanoid` |
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--- |
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## Data Fields |
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The dataset contains the following fields for each instance: |
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* **`image`**: A `PIL.Image.Image` object containing the rendered RGB image ($512 X 512$ pixels). |
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* **`semantic_class`**: A `string` indicating the class of the entity for which the pose is provided (e.g., "mug" or "humanoid"). |
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* **`transform`**: A `string` representing the full $4 X 4$ transformation matrix that maps points from the camera's coordinate frame to the object's local coordinate frame. |
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* **`Tx`, `Ty`, `Tz`**: The translation components (`float`) of the object's pose in metres, extracted from the transformation matrix. |
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* **`rot_x`, `rot_y`, `rot_z`, `rot_w`**: The unit quaternion components (`float`) representing the rotation of the object relative to the camera. |
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--- |
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## Data Splits |
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The data is split into training, validation, and test sets. Critically, the splits were created based on **unique images** (scenes) rather than instances. This ensures that no image seen during training appears in the validation or test sets, preventing data leakage. |
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| Split | Number of Examples | |
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|--------------|--------------------| |
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| `train` | 16,000 | |
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| `validation` | 2,000 | |
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| `test` | 2,000 | |
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--- |
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## How to Use |
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You can load and use the dataset with the `datasets` library. |
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```python |
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from datasets import load_dataset |
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# Load the dataset from the Hugging Face Hub |
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dataset = load_dataset("anonymous-authors-2025/AnonymousDataset") |
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# Access an example from the training set |
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example = dataset['train'][42] |
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image = example['image'] |
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semantic_class = example['semantic_class'] |
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translation_vector = [example['Tx'], example['Ty'], example['Tz']] |
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rotation_quaternion = [example['rot_x'], example['rot_y'], example['rot_z'], example['rot_w']] |
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print(f"Object Class: {semantic_class}") |
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print(f"Translation (m): {translation_vector}") |
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print(f"Rotation (quaternion): {rotation_quaternion}") |
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# To display the image (e.g., in a Jupyter notebook) |
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# image.show() |
