Datasets:
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---
license: mit
size_categories:
- 10K<n<100K
task_categories:
- image-to-3d
- robotics
- image-feature-extraction
- depth-estimation
- image-to-text
- other
modalities:
- image
- tabular
- text
dataset_info:
features:
- name: image
dtype: image
- name: semantic_class
dtype: string
- name: transform
dtype: string
- name: Tx
dtype: float32
- name: Ty
dtype: float32
- name: Tz
dtype: float32
- name: rot_x
dtype: float32
- name: rot_y
dtype: float32
- name: rot_z
dtype: float32
- name: rot_w
dtype: float32
splits:
- name: train
num_bytes: 4106560699
num_examples: 16000
- name: validation
num_bytes: 510934045
num_examples: 2000
- name: test
num_bytes: 513367561
num_examples: 2000
download_size: 2568917592
dataset_size: 5130862305
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# A Synthetic Dataset for Visual Perspective Taking
This dataset ("AnonymousDataset") accompanies the submission **"Towards Social Foundation Models: A Framework and Synthetic Dataset for Grounding Visual Perspective Taking in Robots"**.
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.
The dataset was procedurally generated using **NVIDIA Isaac Sim** and **Omniverse Replicator**, providing high-fidelity RGB images paired with perfect ground-truth pose annotations.
---
## Dataset Details
### Dataset Summary
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.
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.
* **Total Examples:** 20,000 (derived from 10,000 unique scenes)
* **Objects:** `mug`, `xbot_humanoid`
---
## Data Fields
The dataset contains the following fields for each instance:
* **`image`**: A `PIL.Image.Image` object containing the rendered RGB image ($512 X 512$ pixels).
* **`semantic_class`**: A `string` indicating the class of the entity for which the pose is provided (e.g., "mug" or "humanoid").
* **`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.
* **`Tx`, `Ty`, `Tz`**: The translation components (`float`) of the object's pose in metres, extracted from the transformation matrix.
* **`rot_x`, `rot_y`, `rot_z`, `rot_w`**: The unit quaternion components (`float`) representing the rotation of the object relative to the camera.
---
## Data Splits
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.
| Split | Number of Examples |
|--------------|--------------------|
| `train` | 16,000 |
| `validation` | 2,000 |
| `test` | 2,000 |
---
## How to Use
You can load and use the dataset with the `datasets` library.
```python
from datasets import load_dataset
# Load the dataset from the Hugging Face Hub
dataset = load_dataset("anonymous-authors-2025/AnonymousDataset")
# Access an example from the training set
example = dataset['train'][42]
image = example['image']
semantic_class = example['semantic_class']
translation_vector = [example['Tx'], example['Ty'], example['Tz']]
rotation_quaternion = [example['rot_x'], example['rot_y'], example['rot_z'], example['rot_w']]
print(f"Object Class: {semantic_class}")
print(f"Translation (m): {translation_vector}")
print(f"Rotation (quaternion): {rotation_quaternion}")
# To display the image (e.g., in a Jupyter notebook)
# image.show() |