# The Role of High-Fidelity Data: OVER 3D Maps

The performance of all these advanced models hinges on the quality and scale of the data they are trained on. This is where a **dataset from OVER becomes a critical enabler**. [Compared to other prominent datasets](https://docs.google.com/spreadsheets/d/1KYCW6IHiMok04TKIUhYY-qmLbR62Di0nszySlz7ErlY/edit?usp=sharing), Over The Reality provides an unparalleled combination of scale, resolution, and diversity, making it an ideal "textbook of the real world" for training robotics models.

Key advantages include:

* **Massive Scale:** With 145,000 distinct scenes and approximately 72 million images, it dwarfs many other datasets, providing the vast amount of data needed to train robust and generalizable models.
* **High Resolution:** The dataset features high-resolution images (1920x1080 to 3840x2880), which allows models to learn finer details and more accurate geometric relationships.
* **Rich Data Types:** It includes multi-view RGB images and an RGB-D (color + depth) subset. This depth information is crucial for training models on tasks like metric scaling and 3D reconstruction.
* **Environmental Diversity:** By covering both indoor and outdoor scenes, it allows for the training of versatile robots that can operate in a wide variety of environments, unlike more specialized datasets.

This data acts as a foundational training ground:

* **Foundation Vision Models** are trained on this data to learn robust and accurate representations of 3D geometry and semantics from the ground up.
* **World Models** leverage these maps as the basis for creating ultra-realistic simulation environments. Instead of building a simulation from scratch, they can use a perfect digital twin of a real location, ensuring that what a robot learns in simulation will transfer effectively to the real world.
* **VLAs** can be trained within these realistic simulations, allowing them to ground language commands in complex, varied, and physically accurate contexts.


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