ARKit LabelMaker introduces a massive, densely annotated 3D dataset that is over three times larger than previous datasets, potentially enabling a 'GPT moment' for 3D vision.

• Addresses the critical data bottleneck in 3D vision research
• Extends ARKitScenes with comprehensive semantic annotations
• Enables transformer architectures to reach their full potential in 3D understanding
• Creates a foundation for scaling neural networks in spatial computing applications

Why it matters: This research bridges a fundamental gap in engineering AI systems for 3D environments, providing the data scale needed to advance indoor scene understanding for applications in construction, robotics, and AR/VR development.

ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding