🗺️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, 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.